Summary

This dissertation presents a cross-sectional survey exploring burnout in care staff working in dementia-registered residential homes in Cardiff. The aging population and anticipated prevalence of dementia makes this exploration of particular relevance. Stress and burnout among care staff working with people with dementia can result in greater sickness and worse care outcomes.

This survey analyses the responses of 163 staff to questions on dementia knowledge, attitudes and psychosocial stressors. The data are explored to reveal the underlying themes and concepts that are particular to this population.

These concepts are used to produce a model of burnout that has 3 facets, ‘Physical and Emotional Burnout’, ‘Work Burnout’ and ‘Resident Burnout’ and all 3 have ‘Stress’ as their core component. In addition, ‘Physical and Emotional Burnout’ is mediated through ‘Hopeful’ attitudes, while ‘Work Burnout’ and Resident Burnout’ are mediated through ‘Professional’ values.

Further variables associated with ‘Physical and Emotional Burnout’, include exposure to ‘Physical Violence’ in work and less ‘Time in Profession’. ‘Work Burnout’ was also associated with less ‘Time in Profession’ and ‘Resident Burnout’ was negatively associated with having ‘British Ethnicity/Nationality’.

The results of this survey indicate that stress is a central component to burnout as found in previous research. The factors found to mediate burnout also reflect current research on organisational engagement and positive psychological states.

The associations of ‘Time in Profession’, exposure to ‘Physical Violence’ and ‘British Ethnicity/Nationality’ are also relevant to this population and would benefit from further study to explore potential confounders.

Further research into burnout in this population, would benefit from assessment of the direction of causality for the above associations and this could be of use in evaluating interventions to improve these working environments.

Burnout remains an important concept to understand to improve the lives of both care staff and people living with dementia.

Dissertation

An Exploration into Burnout in Care Staff Working in

Dementia-Registered Residential Homes in Cardiff

Submitted by

David Mark Howells

2013

for

MSc Ageing, Health and Disease

Cardiff University

Cardiff, Wales

United Kingdom

Acknowledgement This dissertation has been the culmination of work that has involved a great number of people who have unconditionally offered their time, support and expertise. The origins of this work developed from the ‘Enhanced Dementia Care Project’, a Cardiff County Council-run scheme, funded through a ‘Promoting Independence and Well-being’ grant from the Welsh Assembly Government. Thanks should be given here to Jude Viney and her team for having the faith in my ability to evaluate the project and for providing support and guidance at every stage of the process of the survey development. Thanks should also be made to the project manager, Becky Vangasse for responding to my numerous e-mails on the minutiae of the questionnaire and care home engagement. The entire project team worked tirelessly to make the scheme a success and should be congratulated for maintaining their enthusiasm throughout. Thanks should also go out to the numerous key staff from the care homes that were enthusiastic for improving care for their residents and helped to design the questionnaire and distribute to their colleagues. Thanks also to those care staff responding to the survey, without whom, this evaluation would not have been possible. I hope this work helps you to understand your important role and the need to support each other in your very stressful occupation. Thanks to Dr Marion Gray and Dr Rhiannon Callaghan for their support and the opportunities to develop this project over more years than was originally envisaged! Thanks to Dr Stanley Zammit and Dr John Gallacher for their assistance in illuminating the dark arts of statistical analysis when this was most needed! A great deal of thanks go to my dissertation supervisor, Professor Antony Bayer who has given advice and clarity of thought on the project and provided direction for making the most of this experience. I would also like to thank Dr Win Tadd for suggesting this MSc course 5 years ago, while acting as celebrant for my daughter, Ariana’s naming ceremony. A big thank you to Ariana, who has ensured that my reading has not been too narrow over the course of the last few years. Also a special thank you to my wife, Samantha who has been a far greater support than either of us anticipated was needed and deserves special recognition for her wisdom and humour through a seemingly endless process. I do not know how I will repay you!

Dedication

To the centre of my world, Sam and Ari.

Contents

Chapter 1: Introduction 1

Chapter 2: Background 2

Introduction 2

Dementia 2

Dementia Demographics 3

Care Homes 4

Care Home Population of Older People 4

People Living with Dementia in Care Homes 4

Dementia Registered Care Homes 5

Difficulties in Care 6

Care Staff 7

Care Staff Characteristics 7

Care Staff Characteristics in Cardiff 9

Burnout 10

Consequences of Burnout 11

Factors Associated with Burnout 12

Demographics 12

Personality 14

Mental Health 15

Offensive Behaviour 16

Attitudes 17

Knowledge 18

Psychosocial/Organisational Factors 20

Preventing Burnout 23

Background Summary 25

Chapter 3: Aims and Research Hypothesis 26

Aims of Study 26

Research Hypothesis 28

Aims and Research Hypothesis Summary 28

Chapter 4: Methodology 29

Introduction 29

Design 29

Participants 30

Measures 30

Demographic Information 31

Copenhagen Burnout Inventory 31

Dementia Knowledge Questionnaire 32

Approaches to Dementia Questionnaire 33

Copenhagen Psychosocial Questionnaire 34

Data Analysis 35

Missing Data 35

Demographic Information 36

Descriptive Statistics 36

Exploratory Factor Analysis 36

CBI Associations 38

Logistic Regression 39

Ethical Approval 41

Methodology Summary 42

Chapter 5: Results 43

Introduction 43

Questionnaire Response 44

Demographic Variables 45

Psychometric Properties of Variables 47

Copenhagen Burnout Inventory (CBI) 47

CBI Descriptives 47

CBI Exploratory Factor Analysis 48

CBI Checking Assumptions 48

CBI Factor Extraction 49

CBI 3 Factor Model 50

CBI Summary 55

Dementia Knowledge Questionnaire (DKQ) 56

DKQ Descriptives 56

DKQ Exploratory Factor Analysis 57

DKQ Checking Assumptions 57

DKQ Factor Extraction 57

DKQ 2 Factor Model 58

DKQ Summary 62

Approaches to Dementia Questionnaire (ADQ) 63

ADQ Descriptives 63

ADQ Exploratory Factor Analysis 64

ADQ Checking Assumptions 64

ADQ Factor Extraction 65

ADQ 2 Factor Model 66

ADQ Summary 70

Copenhagen Psychosocial Questionnaire II (COPSOQ) 71

COPSOQ Exploratory Factor Analysis 71

COPSOQ Checking Assumptions 71

COPSOQ Factor Extraction 72

COPSOQ 3 Factor Model 73

COPSOQ: ‘Offensive Behaviour’ 77

COPSOQ Summary 78

Statistical Associations with CBI Factors 79

Demographic Associations 79

Covariate Associations 80

‘Offensive Behaviour’ Associations 81

Multivariate Analysis 82

Checking Assumptions 82

Logistic Regression Associations 83

Logistic Regression of CBI ‘Physical and Emotional Burnout’ 83

Logistic Regression of CBI ‘Work Burnout’ 87

Logistic Regression of CBI ‘Resident Burnout’ 90

Comparing Logistic Regression Burnout Models 93

Graphical Representations of the Burnout Models 94

CBI ‘Physical and Emotional’ Burnout Model 94

CBI ‘Work Burnout’ Model 96

CBI ‘Resident Burnout’ Model 98

Results Summary 101

Chapter 6: Discussion 102

Introduction 102

Critique of Background 102

Critique of Methodology 104

Copenhagen Burnout Inventory (CBI) 106

Dementia Knowledge Questionnaire (DKQ) 107

Approaches to Dementia Questionnaire (ADQ) 107

Copenhagen Psychosocial Questionnaire II (COPSOQ) 108

Demographic Information 109

Data Analysis 110

Critique of Results 111

Burnout as Determined by CBI 113

CBI Descriptives and Factor Analysis 114

Covariate Descriptives and Factor Analysis 117

Demographic Associations 119

Covariate Associations 120

Offensive Behaviour Associations 122

Explorations Using Logistic Regression 123

Logistic Regression of ‘Physical and Emotional Burnout’ 124

Logistic Regression of ‘Work Burnout’ 126

Logistic Regression of ‘Resident Burnout’ 127

Predicting Burnout 129

Discussion 130

Limitations of the Survey 130

Strengths of the Survey 132

Opportunities for Further Research 133

Implications for this Data 133

Implications for this Population 134

Implications for Research Theme 135

Discussion Summary 136

Chapter 7: Conclusion 137

Background 137

Methodology 137

Results: Descriptives 138

Results: Exploration 138

Results: Regression 140

Discussion of Burnout 142

Implications for Further Research 144

References 147

Appendix I 163

Questionnaire Information Leaflet 164

Questionnaire Booklet 167

Appendix II 187

Recoding Demographic Variables 188

Care Homes 189

Sex 189

Age 190

Marital Status 191

Children 192

Education 193

NVQ Level 194

Dementia Training 196

Job Status 197

Time in Current Job 198

Time in Profession 199

Shift Pattern 200

Hours Worked 202

Ethnicity/Nationality 204

Appendix III 205

COPSOQ: ‘Offensive Behaviours’ Frequency 206

Behaviour 207

Bullying: Frequency/Protagonist 207

Sexually Inappropriate: Frequency/Protagonist 208

Threats of Violence: Frequency/Protagonist 209

Physical Violence: Frequency/Protagonist 210

1

Chapter 1: Introduction

This dissertation will explore the concept of burnout of care staff working in dementia

registered residential homes in Cardiff, through a study involving a postal survey of

staff.

The importance of this subject area will be detailed in the background to this

exploration and will help to place the study within current understanding in this field.

The academic literature will be examined to understand the concepts likely to be

associated with burnout in this population and relevant key factors selected for

inclusion in the study. Difficulties in the measurement of these factors will also be

discussed along with the challenges faced in choosing appropriate psychometric

instruments to provide reliable estimates of the selected concepts.

The methodology for conducting the survey will be detailed and will additionally

describe the statistical methods used to assess the suitability of the instruments for

this purpose. The results obtained through following the above steps will enable a

greater understanding of the reliability and applicability of the instruments for each of

the desired concepts in this population.

The aim of this exploration is to produce a simple model to explain burnout, in terms

of the most significant concepts derived from a multivariate regression analysis. The

model of burnout resulting from this exploration will be assessed and the implications

for the participants and wider populations discussed.

2

Chapter 2: Background

Introduction

The background to this dissertation will detail the growing need for residential

dementia care due to ongoing demographic changes in the U.K. population. The

need for a highly skilled workforce in providing good quality dementia care will be

highlighted, including some of the difficulties experienced through caring for people

that may have behavioural difficulties.

The impact of these problems on care staff, including burnout, will be recognised as

well as the role that individual characteristics can play in modifying these. The

academic literature on burnout will be examined with key concepts identified that

influence burnout in comparable work environments.

Difficulties in implementing burnout interventions will be also be described, with

limited research having explored this area in general and this population in particular.

Dementia

Dementia is a term that is used to describe a pattern of neurological impairment,

typically involving deficits of cognition, not least memory. The term captures a

number of different diseases that affect the brain, with Alzheimer’s disease being the

most common, at an estimated 62% of cases (Knapp and Prince 2007). Many of

these illnesses become more prevalent with age, with 1 in 14 people over 65 and 1

in 6 people over 80 having some form of dementia (Knapp and Prince 2007).

3

Dementia Demographics

The ageing population in the U.K. has given rise to increased estimates of dementia,

with associated morbidity, mortality, carer stress and societal costs (Lakey et al.

2012). As the prevalence of dementia in Wales is anticipated to rise, changes in the

provision of services are needed to ensure optimal care for this vulnerable

population (See Figure 1. Knapp and Prince 2007). See also Figure 2. for U.K.

estimates of dementia prevalence from 2006 to 2051 (WAG 2009).

4

Care Homes

Care Home Population of Older People

In the U.K., around 400,000 people have their home as part of a residential complex,

(27,700 people in 1,164 care homes in Wales) equating to 2.8% of all people aged

over 65 years (BGS 2011; Knapp and Prince 2007). The reasons for living in a care

home are typically as a result of the individual needing increased support, a more

common occurrence with advancing age and co-morbid medical conditions. The

changing demographics of the U.K. population suggest that by 2031, 22% will be

aged over 65 and in the next 50 years, demand for care homes may increase by up

to 150% (BGS 2011).

People Living with Dementia in Care Homes

Cognitive impairment leading to difficulty with independent living has been viewed

as, “one of the biggest issues” in Wales (WAG 2011). It has been estimated that a

third to half of all people with dementia live in a care home and approximately 40% of

all care home residents have care needs as a result of dementia (BGS 2011; Knapp

and Prince 2007). Figure 3. illustrates the proportions of people in the UK with late

onset dementia living in residential care and in the community (Knapp and Prince

2007).

5

Dementia Registered Care Homes

The anticipated demands for residential accommodation suggest that increasing

numbers of people will have or develop dementia whilst in a care home. The ability

of these homes to meet the challenges of this population vary, however and 70% of

British geriatricians surveyed believed that management of dementia is sub-optimal

in care homes (BGS 2011).

In Wales, care homes that have a ‘dementia-registered’ status are expected to care

for residents with high levels of dementia-related morbidity (Cardiff County Council

2010). Many private care homes have been criticised as being unable to meet the

care needs of people with dementia, however, not least during episodes of

behavioural disturbance (Ballard et al. 2001).

6

Difficulties in Care

Complications of dementia can include both physical and psychological symptoms

that place significant demands on those carers looking after them. Some of the most

distressing and difficult to manage symptoms involve behavioural disturbance and

can include, “aggression… and psychosis… [with a] risk of 90% across the course of

the illness” (Banerjee 2009, p. 16-17).

The impact of these behaviours is considerable, with 65% of family carers reporting

being exposed to aggression and 16% of this occurring daily (O’Callaghan et al.

2010). It is significant cause of caregiver burden, distress and depression and can

directly result in institutionalisation (Black and Almeida 2004; De Vugt et al. 2005;

Miller et al. 2010).

In care homes, behavioural disturbance is estimated to be present in up to two thirds

of residents with dementia and in hospital, 73% of nursing staff on dementia wards

report having been assaulted (Boustani et al. 2005; O’Callaghan et al. 2010). It has

also been suggested that these behaviours adversely affect the health of care staff

and can increase the risk of burnout, however other studies have failed to find these

effects (Brodaty et al. 2003; Nagatomo et al. 2001; Schmidt et al. 2012)

7

Care Staff

Care Staff Characteristics

Caring for people with dementia is challenging and requires staff that have the

necessary skills to meet these complex demands. Care homes are usually privately-

run companies and vary in their environmental and organisational structure. They do

have common elements, however with care staff of varying grades and experience.

A care home will typically have a greater number of less qualified ‘junior’ staff (who

nonetheless may have extensive ‘hands-on’ experience), supervised by a more

qualified ‘senior’, not infrequently with a nursing background.

The nature and degree of dementia training of care staff is highly variable both within

and between care homes, however there are minimum standards and the care home

has a duty to prove to inspectorate services that they are capable of meeting their

residents’ needs (WAG 2004).

One description of the working conditions of ‘junior staff’ suggests that they, “work

long hours, are poorly paid, receive minimal benefits, and are prone to injury and

depression” and have insufficient training or support (Zimmerman et al. 2005, p. 96).

Other descriptions of assistants in nursing homes, have identified them as a

population particularly vulnerable to burnout, with high rates of turnover, low pay,

limited involvement in decisions and minimal autonomy (Gruss et al. 2004).

8

Greater stress has also been suggested for staff working with more cognitively

impaired residents as well as those on day shifts, where workload is high and with

part-time status (Brodaty et al. 2003). Increased care staff age and more experience

in nursing homes have also been associated with greater strain (relating to

behaviours associated with dementia) and greater age was also associated with

reduced job satisfaction in this sample (Brodaty et al. 2003).

Characteristics of the care homes associated with greater stress in workers include

larger size (greater than 16 beds) and specialised dementia care status, i.e. homes

likely to include people with greater needs relating to dementia (Zimmerman et al.

2005).

9

Care Staff Characteristics in Cardiff

The profiles of care staff and the care homes they work in are changing, not least

due to demographic, economic and service-driven needs. These profiles are also

changing considerably across Cardiff.

In 2008, there were 9 care homes registered for dementia care in Cardiff but with

only 151 of the 485 beds (31%) having the dementia registration (Cardiff County

Council 2008). A survey by Cardiff Local Authority noted wide variations of approach

in the delivery of dementia care, however all homes expressed the desire to develop

‘a person centred care’ model (Cardiff County Council 2008). Care homes in the

survey varied in their design and environment, with some being purpose-built and

others planning improvements and all had a unique mix of resident characteristics

and dementia care needs (Cardiff County Council 2008).

The survey recorded that 41% of care staff had NVQs (National Vocational

Qualifications) and 63% of these had NVQ level 2 (Cardiff County Council 2008).

30% of staff reported having had some form of dementia care training, however the

content of this varied from e-learning or in-house training to training from external

agencies (Cardiff County Council 2008). Staff turnover, consistency of approach

and openness to change were described as being heterogeneous between the

homes and 7 of the 9 Cardiff homes in the survey were specifically documented as

needing improvements in staff supervision (Cardiff County Council 2008).

10

Burnout

The care of people with dementia is recognised as being difficult and stressful,

particularly where behavioural problems are prevalent (Donaldson et al. 1996). For

informal or family carers, this is frequently termed ‘burden’, however in residential

settings, these stressors can contribute to a pattern of exhaustion, known as

‘burnout’ (Sorensen et al. 2006).

‘Burnout’ is considered to be a psychological response of a worker to chronic strain

in their job resulting in negative consequences for both employee and employer. It is

typically thought of as a state that affects people in the human service sector

(possibly as burnout was first described here) and inter-personal strain is placed

centrally to the concept (Borgogni et al. 2012).

Burnout, as described by Maslach (2003), is characterised by the worker

experiencing feelings of exhaustion, cynicism and inefficacy. These three

components are thought to arise through workplace stress, such as excessive

demands, interpersonal conflict and inadequate support (Maslach 2003). The result

is that workers reduce their efficiency to expend the minimum amount of physical

and psychological resources on their day to day tasks (Maslach 2003). The burnout

concept also allows for factors that reduce burnout, with ‘engagement’ being viewed

as key to initiatives that reduce work stress (Maslach 2011; Schaufeli and Salanova

2011).

11

The exact concept(s) that describe the phenomenon of ‘burnout’ are, however

variable, dependent on the model being used and continue to be in flux in the

academic literature on the subject (Cox et al. 2005). A central issue involves the

need to form a consensus on establishing burnout as a distinct concept specific to

employees and independent of exhaustion, stress or affective disorders (Cox et al.

2005). The need to establish burnout as a definitive state (i.e. present or absent) or

as a trait (present to varying degrees) is also outstanding (Cox et al. 2005).

Consequences of Burnout

Burnout has been suggested as predicting employee turnover, ill health and work

efficacy (Maslach et al. 2001). Severe burnout is estimated to be present in over 7%

of the working population in western countries and has major implications in terms of

social, psychological and economic costs (Shirom 2005).

One meta-analysis of studies showed correlations between employee burnout and

negative work performance, in particular relatiing to their role, the organisation and

customer satisfaction (Taris 2006). It has also been suggested that elements of

burnout can also be transferred between workers, through processes of ‘priming’ and

‘empathic identification’ (Bakker et al. 2007)

12

Factors Associated with Burnout

Burnout is believed arise out of an imbalance between the individual and their work

environment. Various models that have been used to understand burnout in the

work environment have examined factors relating to demands, autonomy, support,

justice and effort-reward imbalance (Borritz et al. 2010; Kristensen 2010). The

following sections summarise some of the factors thought to be relevant when

exploring burnout;

Demographics

Individual factors suggested as significant in burnout have included younger age,

male gender, single relationship status and working for less than 2 years (Maslach

2003; Milfont et al. 2008; Zimmerman et al. 2005). Other studies have considered

age to be a minor or inconsequential component in predicting burnout and female

gender to be associated with ‘personal’ burnout and male gender associated with

‘client-related’ burnout (on the Copenhagen Burnout Inventory or CBI scales,

comprising of ‘Personal’, ‘Work-Related’ and ‘Client-Related’ Burnout) (Borritz et al.

2005; Nagatomo et al. 2001; Shirom 2005).

Greater job satisfaction (negatively associated with burnout) has been reported for

those with greater training and in non-white care staff, although turnover is greater

for non-white workers (Rosen et al. 2011; Zimmerman et al. 2005).

Burnout has also shown some familial clustering, however twin studies have

favoured a shared environmental explanation, rather than genetic (Shirom 2005).

13

Other factors potentially related to burnout include ‘socio-economic status’

(supervisor status or advanced education), family status (cohabiting and children at

home), health related lifestyle (smoking, alcohol, exercise, weight) and illness

(Borritz et al. 2010).

Temporary workers have higher psychological distress and worse health outcomes

than permanent workers, as have shift workers compared with regular daytime

workers (Llorens et al. 2010). Working more than 40 hours per week and working

long (greater than 10 hour) shifts has been associated with affective disorders and

burnout (Albert et al. 2013; Llorens et al. 2010). Recovery time, both out of hours

and days off, has also been suggested as a protective factor for burnout (Sonnentag

2005).

Given the above research, including information on demographic factors and working

conditions in this survey may provide valuable information on burnout.

14

Personality

Personality factors have also been associated with burnout, particularly ‘neurotic’

personality traits, as well as those with less social and highly individualistic traits

(Gandoy-Crego et al. 2009; Shimizutani et al. 2008). Other associations with

burnout have included, “openness to changes and anxiety”, with non-burnt out staff

showing traits of, “emotional stability, liveliness, privateness and tension”

(Gustafsson et al. 2009).

The presence of personality traits acting as a confounder should be considered

however, as personality may influence the individual’s self-evaluation (of their

psychosocial work environment) rather than affecting ‘burnout’, such that people with

a negative outlook may hold more negative evaluations of their own coping and

health.

Neuroticism has also been considered a minor component in predicting burnout in

some studies, although this association was increased where the leadership was

based on an ‘autocratic’ style (De Hoogh and Den Hartog 2009; Shirom 2005). In

addition, those with a low ‘internal locus of control’ showed lower burnout where the

leadership style was ‘charismatic’ (De Hoogh and Den Hartog 2009).

15

Mental Health

Depression has been strongly associated with burnout and has also been suggested

as a potentially significant confounder in explaining many of its consequences

(Borritz et al. 2010; Shirom 2005). Taking account of this correlation when designing

burnout research studies has been recommended (Shirom 2005).

Of note, some psychometric instruments used to explore workplace psychosocial

factors (e.g. Copenhagen Psychosocial Questionnaire or COPSOQ (Long Version))

have recognised this confounder and include a section on ‘depressive symptoms’

and this has been significantly associated with sickness absence (Pejtersen et al.

2010).

Both the COPSOQ and the Copenhagen Burnout Inventory (or CBI) contain a

question based on ‘emotional exhaustion’ and this has also been associated with

depressive symptoms, while COPSOQ scales on ‘emotional demands’ were

positively associated and ‘meaning of work’ negatively associated with mental health

problems (Burr et al. 2010; Pejtersen et al. 2010). In other studies, organisational

injustice has been associated with depression (Andersen et al. 2010).

The direction of causality in these models suggest that workplace factors (notably

emotional exhaustion and job satisfaction) have stronger effects on mental health

than mental health does on workplace factors (De Lange et al. 2004).

A potential confounder for mental health problems and burnout, has been suggested

as workplace violence, with this predicting fatigue and emotional demands in

addition to depression (Burr et al. 2010).

16

Offensive Behaviour

Violence in the workplace is classed under a broad category of ‘Offensive

Behaviour’, which can be perpetrated by supervisors, colleagues, subordinates,

service users or other persons. These behaviours can be verbally or physically

aggressive, sexually inappropriate and/or bullying and are associated with greater

rates of turnover, sickness and reduced health and psychological well-being

(Clausen et al. 2012).

Offensive behaviour is reported as being more common in human service

occupations and particularly where the workforce is dominated by a majority of a

single gender, such as nursing (Clausen et al. 2012).

Nurses are an employee group that is frequently subject to verbal and physical

aggression and the frequency of these incidents are associated with burnout in

general, and with ‘depersonalisation’ (emotional distancing) in particular (Winstanley

and Whittington 2002).

One study of care home workers notes that the associations with turnover are

strongest for ‘bullying’ (commonly from colleagues and supervisors) but that these

effects are mediated to an extent by employee ‘well-being’ (Clausen et al. 2012).

17

Attitudes

Employee attitudes to their workplace is thought to have a number of aspects, not

least job satisfaction and organisational commitment, both of which are associated

with burnout (Clausen 2009; Judge and Kammeyer-Mueller 2012). Attitudes are also

viewed as having a good predictive value for intentions and subsequent actions,

proving a useful model for research into specific behaviours (Judge and Kammeyer-

Mueller 2012).

A concern for staff with burnout caring for people with dementia is that the elements

of depersonalization and cynicism could predispose to harmful attitudes that may

lead them to regard patients as objects (Lee et al. 2012). This psychological

distancing may be used as a coping strategy by the individual to protect themselves

against further stress, resulting in them performing tasks mechanically, rather than in

a person-centred manner, thereby avoiding therapeutic interactions, to the detriment

of both parties (Sonnentag 2005).

One study involving residential home care staff did not find associations between

depersonalisation or emotional exhaustion and the quantity or quality of staff-resident

interactions but did find improved interactions with greater ‘personal efficacy’ and

‘involvement in decisions’ (Jenkins and Allen 1998). Hopefully this suggests that the

potential outcome as described above, of cynicism and objectification of residents, is

an extreme and infrequent reaction.

18

Person-centred attitudes have been associated with job satisfaction, particularly

amongst, “staff working in newer facilities and those who feel better trained”

(Zimmerman et al. 2005, p. 102-3). It has also been noted, amongst informal carers

that hopeful attitudes have been associated with less burden and distress and

greater resiliency and social support (Cumming 2011).

Given the potentially important contribution to influencing both burnout and quality of

care, assessment of attitudes towards people with dementia should also be a key

component of this survey.

Knowledge

Dementia knowledge has been linked with attitudes to dementia and through this,

the behaviour of care staff towards people with dementia (Lintern 2001). Knowledge

about dementia has been shown to be highly variable between different grades and

occupations of healthcare employees as well as between specialties (Barrett et al.

1997).

Studies of informal carers have also shown that ‘irrational beliefs’ about dementia

predict depression in the carer, potentially through uncertainty about future

expectations of the illness (Graham et al. 1997). Carers with greater knowledge

were more likely to have reduced expectations of people with dementia and to make

positive comparisons, however they were also more likely to have increased anxiety

(Graham et al. 1997b).

19

The Dementia Knowledge Questionnaire (DKQ), used in the above studies has

received conflicting reports, with some studies failing to show either positive or

negative associations with carer stress (Goncalves-Pereira et al. 2010). Ethnic

differences in DKQ scores have also been seen, with older people of Indian origin

scoring lower on this test than a caucasian sample, in the general population

(Purandare et al. 2007).

Care staff working with people with dementia in care homes and day centres have

demonstrated better care and improved quality of life of their service users with

greater professional knowledge (Kazui et al. 2008). Educational support is

considered important in attaining this goal and educational interventions have been

shown to reduce burden, again in informal carers (Graham et al. 1997).

Knowledge of dementia is frequently assumed to be a vital component for any

professional training course into dementia care, however the evidence from

academic literature is somewhat weak. Dementia knowledge was therefore viewed

as an ‘intuitively’ important factor to include in exploring stress/burnout in care home

staff, however lacks an evidence base with which to anticipate outcomes.

20

Psychosocial/Organisational Factors

The strain of conflicts in employee ‘work-life balance’ has also been suggested as a

predictor of burnout, although the direction of causality may be bidirectional (Brauchli

et al. 2011). Stronger associations with burnout have been observed, however,

between the impact of demands of work on life, rather than life on work (Brauchli et

al. 2011; Fuz et al. 2008). Suggested interventions to address burnout may involve

changing work-related factors, such as reduced hours and increased flexibility and

autonomy over working patterns (Brauchli et al. 2011; Llorens et al. 2010).

Overall, workplace organisational factors, rather than individual characteristics have

been shown to be more significant in predicting burnout, and are thought to include,

“chronically difficult job demands, an imbalance between high demands and low

resources, and the presence of conflict” (Maslach 2003, p. 191).

This was also suggested through associations between stress and work intensity in

staff working in nursing homes, with protective effects described from ‘effective

coping strategies’ (Schmidt et al. 2012; Schmidt and Diestel 2013). Interviews with

nursing staff have also suggested that a source of stress may be related to

discrepancies between the work that staff felt was necessary and the resources that

they had been allocated to complete that work (Edberg et al. 2008).

21

High levels of burnout on all 3 of the CBI’s scales (‘Personal’, ‘Work-Related’ and

‘Client-Related’ Burnout) have been linked with ‘emotional’ and ‘quantitative

demands’, and ‘role conflicts’ and negatively associated with ‘meaning of work’

(Borritz et al. 2005). Elsewhere, ‘role conflicts’ were associated with increased

turnover, with ‘influence’ at work and ‘leadership quality’ reducing this risk (Clausen

et al. 2012).

Poor work ‘predictability’ has been correlated with high ‘Personal’ and ‘Work-Related’

burnout, while ‘emotional demands’ and reduced ‘role clarity’ were associated with

‘Work-Related’ and ‘Client-Related’ burnout (Borritz et al. 2005). Factors that have

been shown to be significant for only ‘Work-Related’ burnout include ‘work pace’,

poor ‘potential for development’, and poor ‘leadership’ (Ibid.).

Many of these factors represent a shift in understanding of organizational stressors

away from task and intensity related to that of inter-personal relationship based

understanding or ‘Social Capital’ (Kristensen 2010). Meaning at work, justice (or

equity) and job satisfaction are considered a key components of this concept and the

concept of ‘affective organisational commitment’, which has been negatively

associated with exhaustion and cynicism amongst nurses (Clausen 2009; Taris et al.

2002).

‘Affective organisational commitment’ has also been associated with employee well-

being, job performance and ability to cope with work stress and inversely associated

with turnover (Clausen 2009; Rosen et al. 2011). Interestingly, greater ‘meaning of

work’ and ‘quality of leadership’ have also been associated with greater ‘Personal’

burnout, potentially as a consequence of it being a protective factor for continuing

work despite higher levels of burnout (Borritz et al. 2005).

22

Leadership has been demonstrated as a vital component in the

psychosocial/organisational environment, with benefits suggested for styles that are,

‘participatory, supporting and/or fair’ and potential harms for styles that are, ‘laissez

faire, autocratic and/or abusive’ and this holds true for nursing homes (Castle and

Decker 2011; Wild et al. 2010; Llorens et al. 2010).

Nursing assistants in care homes that had ‘nonempowered environments’ described

more job-focused stressors than in ‘empowered environments’ and senior

supervision has been suggested as a key factor in retaining these staff (Bishop et al.

2008; Gruss et al. 2004). The benefits here also extended to the care home

residents with greater work commitment corresponding to improved quality of life and

greater satisfaction in their relationships with nursing staff (Bishop et al. 2008).

Further associations have been noted between nursing assistant job satisfaction and

having enough time to do their job, having a challenging role and having satisfactory

working hours (Bishop et al. 2009).

The psychosocial/organisational environment is therefore central to concepts of

workplace burnout and is to undergo further analysis in this project, although the

topic is substantially broad so as to require a clear focus.

23

Preventing Burnout

Some studies have noted that ‘burnout’ is changeable over time, suggesting that an

individual’s susceptibility to burnout is modifiable with the potential to improve well-

being and reduce sickness absence from appropriate interventions (Borritz et al.

2006).

Although research on interventions to reduce burnout is limited, it is suggested that a

combination of strategies to improve both personal and organisational characteristics

would be of greatest benefit (Maslach 2003). Other research has focused on

identifying individuals or even ‘clusters’ of workers at risk of burnout and of

developing targeted interventions based on their individual need (Maslach and Leiter

2008).

Maslach and Leiter (2008) distinguish between those with early warning signs

(significant exhaustion or cynicism), those at a ‘tipping point’ (significantly low

fairness scores) and those already in burnout (significant exhaustion and cynicism)

and makes suggestions for addressing these 3 states.

24

Many interventions have been shown to have no effect on burnout or to actually

have negative effects, particularly in workplace reorganisation where staff have no

active involvement (Anderson et al. 2010; Visser et al. 2008). Another study,

exploring the effects of an intervention in a large hospital, noted more negative

evaluations from staff, including of ‘leadership quality’, ‘supervisor support’ and

‘possibilities for development’ (Aust et al. 2010).

Another significant factor in this intervention, was of staff reporting reduced

‘emotional demands’, although this may be due to disengagement (a feature of

burnout) (Ibid.). The mechanism of these negative observations was considered as

a result of, ‘disappointing expectations’ (Ibid.).

Research into training and/or interventions for both burnout and dementia care is

sparse and very much needed, with some ‘positive psychology’ approaches showing

promise (Elliott et al. 2012; Meyers et al. 2012).

25

Background Summary

Dementia is becoming more prevalent in the U.K. due to the ageing population and

with it comes challenges for those caring for them, not least in care homes. The

amount of research literature on burnout in care staff is poorly representative of the

importance of this field given the morbidity and economic implications. There is

enough of a correlation, however between the available research in dementia care

homes and more generalised research on burnout, to be able to draw some

conclusions.

The academic research suggests that a combination of factors is involved in burnout,

with influence from both individual and organisational elements. This interaction of

factors can be difficult to disentangle and interventions to improve the workplace may

result in unintended negative consequences if inexpertly managed.

26

Chapter 3: Aims and Research Hypothesis

Aims of Study

Burnout of staff working in dementia-registered care homes has substantial

implications for maintaining a healthy and committed workforce, as well as for high

quality resident care. The aim of this study is to explore the burnout concept as it

relates to care staff working in dementia registered residential homes in Cardiff.

The academic literature, as described, identifies a number of individual and

organisational factors that have been linked to burnout. Some of these key factors

are used to examine burnout in this population, with selection based on their

importance to the burnout model and the burden on participants. The risk of

introducing ‘Type I’ statistical errors into the analysis due to excessive data collection

from limited participants is also acknowledged.

Demographic information of care staff has been linked to varying rates of burnout.

The aim for collecting this information is to explore these associations based on a

pragmatic ‘best-fit’ for responses to these questions, rather than theoretical

considerations.

The exploration of both burnout and the factors associated with burnout involves

identifying suitable, validated psychometric instruments to act as proxies for the

underlying concepts. As research in dementia-registered residential homes is

limited, the instruments selected may not have been validated for use in these

populations.

27

The instruments therefore need to be examined using exploratory statistical methods

to establish their factor (or subscale) construction, as determined by the participant

response patterns. These subscales are compared to those described in the

academic literature to assess the applicability of those concepts to this population.

The individual items within each of the subscales are also examined in order to

describe patterns that could reflect underlying concepts within the subscale. This

may be of particular relevance where subscale items for the population are divergent

from those expected to be found from the literature.

An examination of the instruments and demographic information relating to burnout

in this manner aims to provide an overview of the applicability of these factors to this

specific population. The main aim is to produce a burnout model (or models) that

best describe the burnout concept, using the demographic associations and the

derived factor constructs.

The aim of producing the burnout model (or models) is to explore the concept of

burnout through examining those factors found to reflect the underlying concepts for

burnout in this population. It is anticipated that this knowledge will be useful in

further understanding the burnout of care staff in dementia registered residential

homes in Cardiff. The strengths and limitations of the study will be acknowledged

and the information used to suggest further directions for burnout research in these

environments and for a more generalised population.

28

Research Hypothesis

The hypothesis of this research is that burnout, in care staff working in dementia

registered residential homes in Cardiff, varies with certain individual and

organisational factors. The academic literature suggests that dementia knowledge,

attitudes, job satisfaction, leadership, emotional demands and exhaustion are

associated with burnout, along with various demographic variables. This study will

explore the hypothesis that these factors are associated with burnout as described in

the literature and will produce a model (or models) that provide the most

parsimonious explanation of the variability of burnout with these associated factors.

Aims and Research Hypothesis Summary

The aim of this dissertation is to explore the concept of burnout as applicable to care

staff in dementia-registered residential homes in Cardiff. Selected demographic and

conceptual factors that have been suggested by the academic literature to be

associated with burnout will be examined to establish their applicability for use in this

population. These factors include dementia knowledge, attitudes, job satisfaction,

leadership, emotional demands and exhaustion, amongst others.

The hypothesis of this exploration is that through this process, a model will be

produced that explains the concept of burnout in terms of the most significantly

associated factors. The implications of the burnout model for this and other

populations will be discussed.

29

Chapter 4: Methodology

Introduction

This section will detail the methodology followed to explore the concept of burnout in

this population, as influenced by the academic literature on the subject. This will

include information on the design of the study and the process of recruitment, as well

as details of the measures felt to best reflect the variables under examination.

The construction of these measures will be explored to assess their validity and

reliability when used in previous research with comparable populations. The

statistical methods used in data analysis will also be described as this process is

central to understanding the data and its implications.

Design

The design of this observational study was a cross-sectional survey. The method

was a pseudoanonymised questionnaire to be returned by post.

30

Participants

The participants for this survey were care staff working in 18 dementia-registered

residential homes in Cardiff in 2010. Identification of the homes was through the

information resources available to Cardiff Local Authority (Cardiff County Council

2010). Engagement with the care home managers and/or senior staff was at events

organised as part of the ‘Enhanced Dementia Care’ project.

The recruitment of the participants took place in 2 phases, with ‘phase 1’ involving

recruitment from an initial 9 care homes and 6 months later, ‘phase 2’ involving a

further 9 care homes. This division was an artefact of the registration of dementia-

care status in the ‘phase 2’ residential homes only after the study had commenced.

Measures

The questionnaire used in the survey comprised of a number of validated

instruments that had shown good reliability in testing from previous research, as well

as selected demographic information. The outcome variable measuring ‘burnout’

was the ‘Copenhagen Burnout Inventory’. Co-variates used to explore the burnout

concept in this sample were the ‘Dementia Knowledge Questionnaire’, the

‘Approaches to Dementia Questionnaire’ and the ‘Copenhagen Psychosocial

Questionnaire II’.

31

Demographic Information

The demographic information requested from the participants included items that had

been suggested in the academic literature as being significant in assessing burnout

in care staff. The aim was to assess these variables further and to assist in adjusting

for confounding factors in later analysis.

Copenhagen Burnout Inventory

The Copenhagen Burnout Inventory or ‘CBI’ is a psychometric instrument designed

to explore the concept of ‘burnout’ in populations, with components that relate to

people in general (the ‘Personal’ burnout subscale), people in work (the ‘Work-

Related’ burnout subscale) and people in human service work (the ‘Client-Related’

burnout subscale) (Kristensen et al. 2005).

The CBI consists of 19 questions relating to ‘burnout’, answered on a 5-point likert

scale, scoring between 100 (‘Always’) and 0 (‘Never’) in 25-point increments, with 1

inversely scored question. ‘Personal’, ‘Work’ and ‘Resident’ burnout sub-scales

consist of 6, 7 and 6 questions, respectively (with Cronbach’s alpha scores of 0.87,

0.87 and 0.85), although answers have typically been positively skewed towards

lower burnout scores (Ibid.).

The core concept of ‘burnout’, as described by the CBI, relates to, ‘fatigue and

exhaustion’ (Ibid.). Using the CBI in a population of approximately 1900 workers

(with numerous professions in 7 different organisations and locations), both the

‘Personal’ and ‘Work’ burnout subscales show high correlations with a ‘Vitality’ scale

and good correlations with a ‘Mental health’ scale (Ibid.). .

32

All burnout sub-scales (but particularly ‘work’ related) were associated with job

satisfaction and also predicted frequency and duration of sickness absence, sleep

problems, intention to quit and use of painkillers (Ibid.). The greatest negative health

correlations have been found with ‘Personal’ burnout, while ‘Work-Related’ burnout

has had the greatest correlation with long term sickness absence (>9 days) (Borritz

2006; Borritz 2010). Of note, changes in burnout scores across time have also

predicted changes in sickness absence (Borritz 2006).

Dementia Knowledge Questionnaire

The Dementia Knowledge Questionnaire (DKQ) is a psychometric instrument

designed to test the knowledge that carers of people with dementia have about the

condition (Graham 1997). It consists of 4 sections relating to ‘Rudimentary

Knoweldge’, ‘Epidemiology’, ‘Aetiology’ and ‘Symptoms’, with maximum potential

scores of 3, 2, 6 and 8, respectively (Ibid.). The total score out of 19 can be sub-

divided into ‘Irrational Beliefs’ (for scores of 0 or 1 out of 3 on ‘Rudimentary

Knowledge’) and ‘General Knowledge’ (a total of the three other categories, out of

16) (Ibid.).

In interviews with 109 informal carers, higher scores on the ‘General Knowledge’

section of the DKQ was associated with, “lower levels of depression but… higher

rates of anxiety” (Graham 1997b, p. 934). Greater knowledge was also associated

with carer confidence and feelings of competence in care-giving (Graham 1997b).

Other significant carer attributes included having, ‘reduced expectations’ and making,

‘positive comparisons’ of the person with dementia (Graham 1997b, p. 933-934).

33

Approaches to Dementia Questionnaire

The Approaches to Dementia Questionnaire (ADQ) is a psychometric instrument

designed to explore the attitudes and behaviour of care staff towards people with

dementia (Lintern 2001). It consists of 2 scales, derived through factor analysis,

termed ‘Hope’ and ‘Personhood’, which require agreement/disagreement with

statements relating to dementia care, on a 5-point likert scale, scored 1 to 5 (Ibid.).

Total scores range from 19 to 95 and greater scores have been associated with

person-centred attitudes to dementia care style (Ibid.).

The ‘Hope Subscale’ consists of 8 statements (representing optimistic attitudes) and

the ‘Personhood Subscale’ consists of 11 statements (representing respect for

‘individual agency’), both with good internal reliability (Cronbach’s alpha of 0.76 and

0.85, respectively; total score 0.83) but with negative skewness i.e. greater numbers

of higher scores (Personhood > Hope) (Ibid.). Greater scores on the ‘Hope

Subscale’ have been linked with greater social engagement between staff and

residents with dementia, including, “purposeful activities” and “qualitatively better

physical care interventions” (Lintern 2001, p. 15).

There have also been associations between the ADQ and greater dementia

knowledge in staff, as well as observations of greater engagement with residents

(‘Hope Subscale’) and physical care (‘Personhood Subscale’) (Lintern 2001).

34

The ADQ questions were devised in consultation with, ‘experts in the field’ and were

piloted with 20 nurses/care assistants on a dementia care NHS ward before use with

123 care staff, of varying grade and experience, from 5 care homes across the U.K.

(Ibid.). This population is directly comparable to that under examination in Cardiff for

this survey and should therefore have good applicability.

Copenhagen Psychosocial Questionnaire

The Copenhagen Psychosocial Questionnaire II (COPSOQ) is a psychometric

instrument designed to explore the, ‘working conditions, health and well-being’ of

employees. It was developed and validated through surveying ‘representative’

samples of 1858 and 3517 Danish workers, respectively (Kristensen et al. 2002;

Pejtersen et al. 2010). The COPSOQ (Short version) consists of 40 questions,

covering 23 ‘dimensions’ that investigate job stress and satisfaction, and was

produced through reviews of existing questionnaires, theoretical discussions and

statistical analysis (Kristensen et al. 2002).

The questions typically consist of a 5 point likert scale, with responses scoring

between 0 and 4, with some scoring inverted. The dimensions were typically made

up of 2 questions on a particular subject, giving them a range of 0 to 8 points,

however the resulting scores for the 23 dimensions cannot be combined to make a

total score. Cronbach’s alpha scores for the dimensions were not provided for the

short version of the COPSOQ, apart from the work ‘predictability’ scale (0.74) (Ibid.).

35

COPSOQ has been used to explore sickness absence across 8000 randomly

selected Danish residents, with ‘Emotional Demands’ and ‘Role Conflicts’ at work

predicting greater incidence of annual sick leave of 3 or more weeks (Rugulies et al.

2010). Other studies using COPSOQ, in eldercare workers and nurses, have

shown that low scores on ‘Commitment to the Workplace’, ‘Meaning of Work’ and

greater ‘Emotional Demands’ are associated with greater long-term sickness and

intention to leave (Clausen et al. 2010; Li et al. 2010).

Data Analysis

Data collection, input and analysis were completed using SPSS Statistics package

versions 18 and 20.

Missing Data

The data from covariates and any associated subscales were included in the

analysis if the participant had completed at least half of the questions for the relevant

scale, otherwise the data from that section was considered missing. Where data

was missing, but accounted for less than half of the questions, the missing data was

computed using the participant’s mean score for the other questions in that section.

36

Demographic Information

Descriptive statistics were obtained for the demographic information in terms of

numbers and percentages of respondents. The information was then examined and

responders categorised into 2 or 3 divisions according to the most pragmatic split of

the data. This was to enable a sufficient number of participants in each category for

later analysis.

Descriptive Statistics

Descriptive statistics (mean, 95% CI, S.D.) were produced for the dependent (CBI)

and independent (ADQ, DKQ and COPSOQ) variables according to the original

instrument models to enable comparison to results from the academic literature.

Exploratory Factor Analysis

The variables (CBI, ADQ, DKQ and COPSOQ) were examined using exploratory

factor analysis, having first checked assumptions relating to the data including

correlations between items and communalities. Communality refers to the proportion

of the item’s variance that is shared with other variables in the model, with 0

representing no sharing and 1 representing complete sharing (Field 2005, p. 630).

The method of extraction and rotation for the exploratory factor analysis was chosen

based on the anticipated degree of correlation between the resulting factors, based

on descriptions from previous research on the variables.

37

Principle axis extraction with oblimin rotation was chosen where there was assumed

to be a significant correlation between the factors (e.g. for ‘burnout’) and principle

components extraction with varimax rotation was used where no correlation was

assumed.

Factor items that were low or double loaded on the pattern matrix of the analysis

were removed from the factor models. The final models were assessed using

Kaider-Meyer-Olkin (KMO) measure of sampling adequacy and Bartlett’s Test of

Sphericity to ensure that the factors were a good fit for the data. KMO values vary

between 0 and 1, with scores above 0.9 considered, “superb”, reflecting, “distinct

and reliable factors” (Field 2005, p. 640). Bartlett’s Test assesses the hypothesis

that there is no correlation between the items within the factors (i.e. making them

unreliable), with significant scores rejecting this hypothesis (Field 2005, p. 652).

The factors produced through this process were examined to assess for a coherent

underlying concept with each factor given a label to approximate this. Items that

comprised an extracted factor were used at a later stage of the analysis by

calculating the mean of the component items that corresponded to each factor at a

loading of > 0.4. This was done, rather than using factors derived from items

weighted according to loadings, due to the desire to have clearly identified factors

influenced only by items that strongly correlate. This method does increase the

degree of correlation between the non-weighted factors, however as they are not

being used in analysis to explain variance between each other, the potential for bias

is reduced.

38

The non-weighted factors’ properties were further assessed using Cronbach’s Alpha

to check for internal consistency (>0.7 being considered acceptable) within and

Spearman’s rho to check correlations between the factors (Field 2005, p. 668). This

was to ensure that the factors were suitable for further analysis of this population.

The normality of the factors’ data distributions were checked using the Shapiro-Wilk

test (a significant result confirming non-normal data), directing the statistical testing

that could be used for further analysis i.e. parametric or non-parametric.

CBI Associations

The statistical associations between the CBI factors and the other variables were

explored using non-parametric methods, namely Mann-Whitney U test for 2

categorical variables and the Kruskall-Wallis test for 3 categorical variables.

For the demographic and ‘Offensive Behaviour’ variables, associations were

explored using the 3 CBI factors, however for the covariates, the CBI factors were

transformed into bivariate variables. This was through classification of the factors

into ‘low’ or ‘high’ burnout based on roughly even numbers of responders in each

category and also enabled assessment of the burnout models using bivariate logistic

regression.

39

Logistic Regression

The statistical characteristics of the 3 CBI factors suggested that an appropriate

method of multivariate analysis would be logistic regression.

As logistic regression makes uses bivariate categorical values as the dependent

variable, the significantly associated categorical variables were cross-tabulated to

ensure a sufficient number of responders in each subsection (low numbers results in

large standard errors in the analysis) (Field 2005, p. 264).

The models were refined through an iterative process of ‘Backward: Likelihood Ratio’

logistic regression analysis to produce the most parsimonious model available (Field

2005, p. 227). The process involved retaining components that significantly

explained variance in the model and removing the items that were least statistically

significant one at a time.

The resulting models have a ‘B’ value and S.E., which are the ‘log-odds’ and

standard error for predicting the dependent variable form the independent variable.

The Wald (Chi-square) value and significance (2-tailed p value) tests the ‘null

hypothesis’ that the variable has no effect on the model. Exp(B) is the odds ratio of

the predictor and where the 95% C.I. (Confidence Interval) crosses 1, the reliability

of the variable is in question and its’ generalizability is limited (Field 2005, p. 254).

Classification tables for the logistic regression were produced, giving the

percentages of the ‘High’, ‘Low’ and ‘Overall’ burnout scores correctly predicted from

their respective model.

40

Once the final model had been established, outlying responses for ‘Standardized

Residuals’ and ‘Leverage’ were noted and a boxplot produced to represent the range

of values.

‘Standardized Residuals’ outliers represent responses in the logistic regression that

were a poor fit for the model, with values above 3 being of concern and those above

2.5 requiring closer examination (Field 2005, p. 246). ‘Leverage’ outliers represent

responses in the logistic regression that were excessively influential in the model,

with values greater than 0.03 (for 4 predictors in the model) or 0.025 (for 3

predictors) requiring closer examination (Ibid.). Further assessment or adjustment of

residual data was not attempted in this analysis.

The 3 burnout models, produced through logistic regression, were compared. The

‘Omnibus Test of Model Coefficient’ produced a Chi-squared statistic for each model

with a significance level, giving the probability of obtaining the statistic if the

combined influence of the independent variables had no predictive effect on the

dependent variables (Field 2005, p. 237). A significance of p<0.001 would be

considered highly significant, i.e. the null hypothesis would be highly unlikely.

The ‘Nagelkerke R Square’ values represent ‘pseudo R-square’ values to

approximate an explanation of variance that the independent variables have on the

model. The score ranges from 0 to 1, representing zero to a complete explanation of

the dependent variable by the model.

41

The 3 models were checked for multi-collinearity using the Tolerance test, (score

>0.1 excluding collinearity) to ensure that no one factor was having a

disproportionate effect on the regression model (Field 2005, p. 175). The goodness-

of-fit of the data was assessed using the Hosmer and Lemeshow Test with non-

significant scores suggesting that the model is a good fit for the data (Field 2005, p.

254).

The components of the burnout model were documented and the most significant

factors were represented graphically to enable observational assessment of the

relationships between the variables. Further graphical representations of key

variables from the model were shown where it was felt that this would help in

explaining the observed trends further.

Ethical Approval

Ethical approval was discussed during the initial stages and throughout the process

of designing the survey. The design was discussed with the Local Research Ethics

Committee who advised that this survey was part of a ‘Service Evaluation’ (as

opposed to research) and would therefore not require ethical approval. In addition,

the survey did not involve NHS personnel and only involved those willing and able to

complete the survey, with consent being implied by the returning of the questionnaire

booklet, clearly stating that the participant was under no obligation to do so (See

Appendix I.). The mandate for completing this survey was provided by the EDC

Project Group, who oversaw the progress of its design, production and delivery.

42

Methodology Summary

This section, on methodology has sought to describe the process used to explore the

concept of burnout in the population of care staff working in dementia-registered

residential homes in Cardiff. Demographic information on the responders has been

collected along with measures that have been demonstrated as being valid and

reliable for use in producing data on burnout and associated concepts.

The distributions and associations of these data were analysed as described and the

results of this analysis will be described in the following section.

43

Chapter 5: Results

Introduction

This section will detail the results of the survey in 3 broad sections; firstly respondent

characteristics, then factor analysis of psychometric instruments and finally

multivariate analysis using these variables to produce a prediction model of burnout.

Demographic information was collated, assessed and pragmatically split to form the

smallest number (typically 2) of roughly equal participant groups to aid later analysis.

Exploratory factor analysis was used to greater understand the psychometric

instrument properties by dividing the variable into independent (but frequently

correlated) factors based on groupings of questionnaire items. These derived

factors reflect underlying concepts from response patterns and it is because these

factors are highly specific to this population that they are used in later analysis.

A model for predicting burnout was then produced from further analysis of the

demographic information and covariate factors. This model explores the influence of

these factors according to those found to be most significant on logistic regression.

44

Questionnaire Response

Of the 531 questionnaires sent, 163 (31%) participants returned their questionnaires

in the pre-paid envelopes. Of these, 95 out of 235 (40%) were in ‘Phase 1’ and 68

out of 296 (23%) were in ‘Phase 2’. The response rates for individual care homes

ranged from 6% to 75% (2/34 to 18/24).

The majority of questionnaires were fully complete, although some responders did

miss individual items. Only 1 questionnaire was returned completely blank, however

some participants missed certain sections and these were treated as missing data.

45

Demographic Variables

The divisions of the recoded demographic variables are detailed in Figure 4. and

further in Appendix II.

46

47

Psychometric Properties of Variables

Copenhagen Burnout Inventory (CBI)

The results from using the CBI to model participant burnout in this survey will be

explored. The descriptive statistics from an unmodified model of the CBI will initially

be detailed, with additional analysis being used to explore the concepts as applicable

to this population.

CBI Descriptives

The data were explored using the item groupings as described in the original 3 CBI

scales; 'Personal', 'Work-Related' and 'Resident-Related' Burnout. These descriptive

statistics are detailed in Figure 5., with correlations in Figure 6.

48

CBI Exploratory Factor Analysis

The conceptual dimensions underlying the CBI in this population were examined

using factor analysis, as it was felt that this would provide valuable information for

understanding burnout in this population. Exploratory factor analysis of the 19 CBI

items was performed using principle axis extraction with oblimin rotation.

CBI Checking Assumptions

Spearman’s rho correlations of the individual CBI items confirmed highly significant

(p<0.000) correlations between the majority of the items and all except one item

having significant (p<0.05) inter-item correlations. Communalities between the items

were all good (0.342 to 0.780) apart from one item (the same item as noted above),

scoring 0.141.

49

CBI Factor Extraction

The initial extraction of 3 factors was based on the 3 factor structure of the CBI,

using all 19 response categories giving a cumulative variance of 63.9%. Only one

category, ‘Resident’-related burnout, contained all the items corresponding to a factor

from the original research.

Further extraction was based on dimensions having eigenvalues greater than 1 and

the appropriateness of this method was checked using a scree plot (See Figure 7.)

as a visual tool to ensure this method did not over or under estimate the number of

applicable and relevant dimensions.

The components of the 3 dimensions were examined using a pattern matrix of the

extracted responses to identify items that had low loadings for all factors or dual

loadings greater than 0.4.

50

One of the response items (‘Do you have enough energy for family and friends

during leisure time?’) had a low loading on all of the 3 dimensions (highest value

0.261) and was the same item that did not fulfil the assumptions noted above and

was therefore excluded from the analysis.

This produced a pattern matrix that included an item (‘How often do you think: “I can’t

take it anymore”?’) that had a further item with low (<0.4) loadings and this was also

removed.

This resulted in a pattern matrix with one item (‘Do you feel that every working hour

is tiring for you?’) ‘dual-loading’ of >0.4 on 2 factors (0.455 for factor 1 and 0.428 for

factor 2) and this was removed.

Excluding these items increased the variance to 68.3% but increased the

correlations between factors (range: 0.419-0.556, compared with 0.356-0.547) (See

Figures 8. and 9.).

CBI 3 Factor Model

The final solution of 16 items in 3 dimensions proved to be a good model for the

data, as demonstrated by a Kaider-Meyer-Olkin measure of sampling adequacy of

0.914 and a Bartlett’s Test of Sphericity (Chi-Square) of 1502.235 (p< 0.000).

Factor 1 contained responses from 7 questions, comprised of 5 from ‘Personal’

Burnout’ and 2 from ‘Work-Related’ Burnout. Factor 2 contained all 6 questions from

‘Resident-Related burnout’ and factor 3 contained 3 questions from ‘Work-Related’

Burnout.

51

For ease of reference and as a summary of the components, factor 1 was labelled,

‘Physical and Emotional Burnout’ (or ‘P&E Burnout’); factor 2, ‘Resident Burnout’ and

factor 3, ‘Work Burnout’.

52

These 3 factors were used at a later stage of the analysis by calculating the mean of

the component items that corresponded to each factor at a loading of > 0.4 (See

Figures 10., 11., 12. and 13.).

53

54

The correlations between the weighted and non-weighted factors were examined,

showing excellent correlations (>0. 942) for the corresponding factors (See Figure

14.). Correlations between the 3 non-weighted factors were moderate to high,

reflecting the shared underlying concept of the 3 CBI factors.

Cronbach’s Alpha for the original 19 items of the CBI was 0.933 and for the 16 item

model, 0.929. For ‘Physical and Emotional Burnout’, Cronbach’s Alpha was 0.897;

for ‘Resident Burnout’, 0.882 and for ‘Work Burnout’, 0.837. These scores suggest

that the 3 factors have internally consistent responses and are therefore acceptable

to use in further analysis.

55

The distribution of the data was checked for normalcy through the Shapiro-Wilk test,

with all 3 of the CBI factors confirmed as having non-normally distributed data (See

Figure 15.). This pattern persisted despite transformation of the factors using log10,

square-root and reciprocal methods.

CBI Summary

The Copenhagen Burnout Inventory was assessed to explore its’ psychometric

properties, with 3 factors identified as being optimal through visual inspection of the

scree plot on exploratory factor analysis. Contributions to the 3 dimension model

were assessed and items removed for low or double loading.

The 3 dimensions were composed of a total of 16 items, explaining 68.3% of the

variance in responses and were labelled ‘Physical and Emotional Burnout’, ‘Resident

Burnout’ and ‘Work Burnout’. Questions highly loaded (>0.4) on each of the factors

were used to represent the factors in subsequent analysis.

56

Dementia Knowledge Questionnaire (DKQ)

The results from using the DKQ to assess the dementia knowledge of the

participants in this survey will be explored. The descriptive statistics from an

unmodified model of the DKQ scales will initially be detailed, with additional analysis

being used to explore the concepts as applicable to this population.

DKQ Descriptives

The data were explored using the item groupings as described in the original 2 DKQ

scales, 'Irrational Beliefs' and 'General Knowledge' (See Figures 16. and 17.).

57

DKQ Exploratory Factor Analysis

The conceptual dimensions underlying the DKQ in this population were examined

using factor analysis. Exploratory factor analysis of the 19 DKQ items was

performed using principle axis extraction with oblimin rotation.

DKQ Checking Assumptions

Correlations of the responses showed that most items had numerous significant

(p<0.05) correlations, however one item required exclusion due to lack of variance

(all participants correctly recognised poor memory as being a symptom of dementia).

DKQ Factor Extraction

The conceptual dimensions underlying the DKQ were examined using exploratory

factor analysis and extraction based on eigenvalues >1 suggested a 5 factor model.

The pattern matrix contained 4 ‘dual-loaded items’ however, and visual assessment

of the scree-plot strongly suggested a 2-factor solution (See Figure 18.).

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The initial extraction of 2 factors, using all 18 response categories explained a

cumulative variance of 34.7% with factors divided broadly between causes and

symptoms of dementia. The component loadings of the 2 factors were examined to

exclude factors that had low or dual loadings, increasing the explained variance to

53.3% (See Figure 19.).

DKQ 2 Factor Model

The final solution consisting of 10 items in 2 dimensions proved to be a good model

for the data, as demonstrated by a Kaider-Meyer-Olkin measure of sampling

adequacy of 0.817 and a Bartlett’s Test of Sphericity (Chi-Square) of 494.332

(p<0.000)

All items from the ‘Irrational Beliefs’ subscale were excluded during the analysis

leaving factor 1 with responses from 6 items from the ‘General Dementia Knowledge’

subscale (1 item on epidemiology, and 5 items on symptoms).

59

Factor 2 contained 4 items from the ‘General Dementia Knowledge’ subscale (2

items on aeitiology and 2 items on symptoms). As the items of the extracted factors

had a slightly different emphasis on type of knowledge, factor 1 was labelled,

‘Symptom Factor’ and factor 2, ‘Aeitiology Factor’.

The correlation between the two factors was moderate to high (0.512) demonstrating

a small degree of independence between the underlying concepts of the factors.

The 2 extracted factors did not have underlying concepts that could easily be

identified to explain the division, however the pattern of responses suggests that the

‘Symptom Factor’ questions were more frequently answered correctly than the

‘Aeitiology Factor’ questions (See Figures 20., 21. and 22.).

The observation was made that the 2 factors may therefore represent a coherent

division in difficulty of knowledge, rather than the original division between ‘Irrational

beliefs’ and ‘General knowledge’.

60

The 2 factors were used at a later stage of the analysis by calculating the sum of the

component items that corresponded to each factor loading > 0.4.

61

The correlations between the weighted and non-weighted factors were examined,

showing high correlations (>0.902) between the corresponding factors (See Figure

23.). Correlation between the 2 non-weighted factors was moderate (0.543).

Cronbach’s Alpha for the original 19 items of the DKQ was 0.792 and for the 10 item

model, 0.827. For the ‘Symptom Factor’, Cronbach’s Alpha was 0.809 and for the

‘Aeitology Factor’, was 0.711. These scores suggest that the 2 factors have

internally consistent responses and would be considered acceptable to use in further

analysis.

62

The distribution of the data was checked for normalcy through the Shapiro-Wilk test,

with neither of the DKQ factors having statistically normally distributed data (See

Figure 24.).

DKQ Summary

The Dementia Knowledge Questionnaire was assessed to explore its’ psychometric

properties with 2 factors identified as being optimal through visual inspection of the

scree plot on exploratory factor analysis. Contributions to the 2 dimension model

were assessed and items removed where low or dual-loaded. The 2 dimensions

were composed of a total of 10 items, explaining 53.3% of the variance in responses

and were labelled ‘Symptom Factor and ‘Aeitiology Factor’. The 2 factors were not

clearly differentiated in their component items according to subject matter, but may

represent differences in the difficulty of the underlying question subsets. Questions

highly loaded (>0.4) on each of the factors were used to represent the factor in

subsequent analysis.

63

Approaches to Dementia Questionnaire (ADQ)

The results from using the ADQ to assess staff attitudes towards people with

dementia in this survey will be explored. The descriptive statistics from an

unmodified model of the ADQ scales will initially be detailed, with additional analysis

being used to further explore the concepts as applicable to this population.

ADQ Descriptives

The data were explored using the item groupings as described in the original 2 ADQ

scales, 'Hope' and 'Personhood'. These descriptive statistics are detailed in Figure

25. Spearman’s rho correlation between the two subscales was 0.243 (p=0.002,

n=155).

64

ADQ Exploratory Factor Analysis

The conceptual dimensions underlying the ADQ for this population were examined

using factor analysis. Exploratory factor analysis of the 19 items was performed

using principle components extraction with varimax rotation. This method was

chosen as previous research had demonstrated a low correlation between the

extracted factors, suggesting that the underlying concepts are largely independent of

each other.

ADQ Checking Assumptions

The independence of the dimensions was also noted when exploring correlations

between the items representing the suggested factors, with poorly significant

associations between many of the responses.

65

ADQ Factor Extraction

Extraction based on eigenvalues >1 suggested a 5 factor model, however the rotated

component matrix contained 4 ‘dual-loaded items’ and visual assessment of the

scree-plot strongly indicted a 2-factor solution (See Figure 26.).

The initial extraction of 2 factors, using all 19 response categories explained a

cumulative variance of 42.6%. The pattern of items corresponded to those

suggested by the authors apart from one item (‘It doesn’t matter what you say to

people with dementia as they forget anyway?’) that loaded more strongly on the

‘Hope’ subscale, than on the ‘Personhood’ subscale.

The component loadings of the 2 factors were examined using to exclude items that

had low loadings or dual loadings. 1 item was removed (‘It is important to have a

very strict routine when working with dementia sufferers’) for low loading (0.290),

increasing the explained variance to 44.0% (See Figure 27.).

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ADQ 2 Factor Model

The final solution of 18 items in 2 dimensions proved to be a good model for the data

as demonstrated by a Kaider-Meyer-Olkin measure of sampling adequacy of 0.783

and a Bartlett’s Test of Sphericity (Chi-Square) of 861.73 (p<0.000).

Factor 1 contained 10 items, all from the ‘Personhood’ subscale, while factor 2

contained 7 of the 8 items from the ‘Hope’ subscale and 1 item from the

‘Personhood’ subscale. As the construction of the extracted factors broadly matches

the subscales as suggested by previous research, factor 1 will be labelled the

‘Personhood Factor’ and factor 2, the ‘Hope Factor’.

The correlation between the two factors was low (0.109) demonstrating a high

degree of independence between the underlying concepts of the factors.

67

The 2 factors were used at a later stage of the analysis by calculating the sum of the

component items that corresponded to each factor at a loading of > 0.4 (See Figure

28., 29. and 30.).

68

69

The correlations between the weighted and non-weighted factors, were examined,

showing high correlations (>0.982) between the corresponding factors (See Figure

31). Correlation between the 2 non-weighted factors was also low (0.185) and

indicates a good independence between the 2 factors.

Cronbach’s Alpha for the original 19 items of the ADQ was 0.799 and for the 18 item

model, 0.802. Cronbach’s Alpha for the ‘Hope Factor’ was 0.787 and for the

‘Personhood Factor’ was 0.818. These scores suggest that the 2 factors have

internally consistent responses and would therefore be considered acceptable to use

in further analysis.

70

The distribution of the data was checked for normalcy through the Shapiro-Wilk test,

with the ‘Hope Factor’ having statistically normally distributed data and the

‘Personhood Factor’ having non-normally distributed data (See Figure 32.).

ADQ Summary

The Approaches to Dementia Questionnaire was assessed to explore its’

psychometric properties with 2 factors identified as being optimal for exploratory

factor analysis. Contributions to the 2 dimensional model were assessed and 1 item

was removed due to a low individual loading. The 2 dimensions were composed of a

total of 18 items, explaining 44.0% of the variance in responses and were labelled,

‘Hope Factor’ and ‘Personhood Factor’. The factors had a low correlation,

suggesting the underlying concepts are likely to have a high degree of

independence. Questions highly loaded (>0.4) on each of the factors were used to

represent the factor in subsequent analysis.

71

Copenhagen Psychosocial Questionnaire II (Short Version) (COPSOQ)

The results from using the COPSOQ to explore the psychosocial work factors of the

participants in this survey will be explored. The descriptive statistics from an

unmodified model of the COPSOQ scales (covering 23 dimensions) will not be

detailed here as the results are poorly comparable with the additional analysis being

used to further explore the broad underlying concepts as applicable to this

population.

COPSOQ Exploratory Factor Analysis

The conceptual dimensions underlying the COPSOQ in this population were

examined using factor analysis. Exploratory factor analysis of the 40 COPSOQ

items was performed using principle axis extraction with oblimin rotation. This

method was chosen as there is assumed to be significant correlations between the

items given their ‘psycho-social’ content.

COPSOQ Checking Assumptions

Correlations of the items showed highly significant (p<0.001) associations between

many of the individual responses as well as numerous significant correlations

between the 23 dimensions.

72

COPSOQ Factor Extraction

An initial extraction of 23 factors was based on the 23 dimensions described in the

academic literature, using all 40 items. This model explained a cumulative variance

of 92.9%, however the 23 factors corresponded to the anticipated dimensions on a

small number of instances only and visual inspection of the scree plot from this

model suggested that a 3 factor model would be optimal.

Next, eigenvalues > 1 were used for extraction and resulted in a 10 factor model,

explaining 72.8% of the variance, but again the optimal model from visual inspection

of the scree plot was of 3 factors (See Figure 33.).

The 3 factor model, using all 40 items, explained 50% of the variance. Additional

analysis was performed to exclude items that had low or dual-loadings, increasing

the explained variance to 63.1% (See Figure 34.).

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COPSOQ 3 Factor Model

The final solution consisting of 29 items in the 3 dimensions proved to be a good

model for the data, as demonstrated by a Kaider-Meyer-Olkin measure of sampling

adequacy of 0.917 and a Bartlett’s Test of Sphericity (Chi-Square) of 3393.709

(p<0.000). Factor 1 contained responses from 16 questions, factor 2, 8 questions

and factor 3, 5 questions. As a broad summary of the components, factor 1 was

labelled as, ‘Culture Factor’; factor 2 as, ‘Stress Factor’ and factor 3 as, ‘Professional

Factor’. The 3 factors showed a moderate degree of correlation, ranging from 0.179

to 0.416 (See Figure 35.).

74

The 3 factors were used at a later stage of the analysis by calculating the sum of the

component items that corresponded to each factor at a loading of > 0.4 (See Figures

36., 37., 38. and 39.).

75

76

The correlations between the weighted and non-weighted factors were examined,

showing excellent correlations (>0. 959) between the corresponding factors (See

Figure 40.). Correlations between the 3 non-weighted factors were variable

(between 0.299 and 0.660), indicating a limited degree of independence between the

underlying concepts.

Cronbach’s Alpha for the original 40 items of the COPSOQ II was 0.855 and for the

29 item model, 0.886. For ‘Culture Factor’, Cronbach’s Alpha was 0.957; ‘Stress

Factor’, 0.813 and ‘Professional Factor’, 0.855. These scores suggest that the 3

factors have internally consistent responses and would be considered acceptable to

use in further analysis.

77

The distribution of the data was checked for normalcy through the Shapiro-Wilk test,

with none of the COPSOQ factors having statistically normally distributed data (See

Figure 38.).

COPSOQ: ‘Offensive Behaviour’

COPSOQ II items relating to exposure to ‘Offensive Behaviour’ were not included in

the 3 factor model due to low loadings. The components of ‘Offensive Behaviour’,

(‘Sexually Inappropriate’, ‘Threats of Violence’, ‘Physical Violence’ and ‘Bullying’)

have been included here, however, due to their theoretical associations with burnout.

The majority of responses for all ‘Offensive Behaviour’ categories was either ‘No’ or

‘A few times’, indicating the behaviours are relatively rare, therefore the response

categories to exposure were classified as either, ‘No’ or ‘Yes’ (See Figure 42.). In

addition, approximately 90% of responders had never been exposed to bullying or

sexually inappropriate behaviour, such that further analysis may be compromised by

low numbers. See Appendix III. for a more comprehensive breakdown of results

relating to offensive behaviour.

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COPSOQ Summary

The Copenhagen Psychosocial Questionnaire (Short Version II) was assessed to

explore its’ psychometric properties, with 3 factors identified as being optimal through

visual inspection of the scree plot on exploratory factor analysis. Contributions to the

3 dimension model were assessed and items removed where low or dual-loaded.

The 3 dimensions were composed of a total of 29 items, explaining 63.1% of the

variance in responses and were labelled, ‘Culture Factor’, ‘Stress Factor’ and

‘Professional Factor’. Questions highly loaded (>0.4) on each of the factors were

used to represent the factors in subsequent analysis.

Questions relating to ‘Offensive Behaviour’, whilst not included in the COPSOQ 3-

factor model, have been considered important in previous burnout research and

were therefore recognised as additional variables for inclusion in subsequent

analysis.

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Statistical Associations with CBI Factors.

Demographic Associations

The 3 CBI-derived factors were explored for statistically significant differences

between the recoded groups of the demographic variables. Figure 43. details the

significance level of the associations and also lists the mean values of the burnout

factor according to the demographic group. The groups are termed ‘low’ or ‘high’

due to the differing terminology used for the groups in the demographic variables.

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Covariate Associations

The 3 CBI-derived factors, were pragmatically divided into ‘high’ or ‘low’ scores to

facilitate exploring statistically significant differences between the factor-derived

scores for the covariates. As most (apart from ADQ ‘Hope Factor’) of the factors had

previously been demonstrated as being non-normal in distribution, Mann-Whitney U

tests were used throughout for a consistent and more conservative approach.

Figures 44. to 46. detail the significance level of the covariate associations and also

lists the mean values of the covariates according to the ‘high’ or ‘low’ burnout factor.

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‘Offensive Behaviour’ Associations

Non-parametric tests of significance (Mann-Whitney U) using the presence or

absence of the COPSOQ: ‘Offensive Behaviours’ for each of the CBI factors was

performed. Figure 47. details the significance level of the associations and also lists

the mean values of the burnout factor according to the behaviour group.

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Multivariate Analysis

Multiple regression analysis produced a model that best predicts care staff burnout.

The 3 CBI factors were used to construct 3 burnout models that had the most

‘parsimonious’ fit from the significantly associated variables.

Checking Assumptions

The 3 derived CBI factors, were used in turn as dependent variables. Assumptions

for using the 3 factors were checked, including the normalcy and homoscedasticity of

the spread of the data, such that linear regression could not be used for multivariate

analysis (Field 2005, p. 203). Logistic regression was therefore selected as an

appropriate method of analysis.

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Logistic Regression Associations

Significant variables for each of the 3 CBI factors were used to estimate which

elements were likely to have a significant contribution to the logistic regression

models under exploration.

Logistic Regression of CBI ‘Physical and Emotional Burnout’

Figure 48. summarises the key variables associated with the CBI factor, ‘Physical

and Emotional Burnout’.

84

Figure 49. shows that there were an adequate number of responders according to

each category in the model, given that there were 3 categorical variables.

Figure 50. shows the logistic regression model for the CBI factor, ‘Physical and

Emotional Burnout’.

85

Figure 51. shows the classification table for this model in predicting ‘High’ or ‘Low’

CBI ‘P&E Burnout’.

86

Figures 52. and 53. show boxplots for Standardized Residuals and Leverage Values.

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Logistic Regression of CBI ‘Work Burnout’

Figure 54. summarises the key variables associated with the CBI factor, ‘Work

Burnout’.

Figure 55. shows that there were an adequate number of responders according to

each category in the model, given that there were 2 categorical variables.

88

Figure 56. shows the logistic regression model for the CBI factor, ‘Work Burnout’.

Figure 57. shows the classification table for this model in predicting ‘High’ or ‘Low’

CBI ‘Work Burnout’.

89

Figures 58. and 59. show boxplots for Standardized Residuals and Leverage Values.

90

Logistic Regression of CBI ‘Resident Burnout’

Figure 60. summarises the key variables associated with the CBI factor, ‘Resident

Burnout’.

The categorical variables of ‘Welsh Nationality’, as well as ‘Sexually Inappropriate’

and ‘Bullying’ behaviours were excluded from further analysis due to low numbers of

responders in some of the subcategories. Figure 61. shows that there were an

adequate number of responders according to each category in the model, given that

there were 2 categorical variables.

91

Figure 62. shows the logistic regression model for the CBI factor, ‘Resident Burnout’.

Figure 63. shows the classification table for this model in predicting ‘High’ or ‘Low’

CBI ‘Resident Burnout’.

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Figures 64. and 65. show boxplots for Standardized Residuals and Leverage Values.

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Comparing Logistic Regression Burnout Models

The 3 models based on the CBI burnout factors and associated variables are

summarized in Figure 63.

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Graphical Representations of the Burnout Models

CBI ‘Physical and Emotional Burnout’ Model

The CBI ‘Physical and Emotional Burnout’ model was explored graphically using the

variables previously found to be most influential in the model and these are shown in

Figures 67. to 69.

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96

CBI ‘Work Burnout’ Model

The CBI ‘Work Burnout’ model was explored graphically using the variables

previously found to be most influential in the model and these are shown in Figures

70. to 72.

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98

CBI ‘Resident Burnout’ Model

The CBI ‘Work Burnout’ model was explored graphically using the variables

previously found to be most influential in the model and these are shown in Figures

73. to 75.

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100

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Results Summary

The results section has described the analysis of data collected from a questionnaire

survey of care staff in dementia-registered care homes in Cardiff. The constructions

of the psychometric instruments have been explored and factors derived, based on

underlying concepts that are specific to this population.

The factors have included 3 separate measures of ‘burnout’ and these have been

analysed to establish significant associations with other factors and demographic

variables. These associations have been included in a process of logistic regression

to produce a ‘parsimonious’ explanatory model for each of the 3 burnout measures.

CBI ‘Physical and Emotional Burnout’ was best predicted by a model that included

covariate factors, COPSOQ ‘Stress Factor’ and ADQ ‘Hope Factor’, and the

variables, ‘Offensive Behaviour: Physical Violence’ and ‘Time in Profession’.

CBI ‘Work Burnout’ was best predicted by a model that included covariate factors,

COPSOQ ‘Stress Factor’ and COPSOQ ‘Professional Factor’ and demographic

variable, ‘Time in Profession’.

CBI ‘Resident Burnout’ was best predicted by a model that included covariate

factors, COPSOQ ‘Stress Factor’ and COPSOQ ‘Professional Factor’ and

demographic variable, ‘Ethnicity/Nationality’.

Further exploration was through identification of scatter-plot trends using the main

components detailed for the above logistic regression models.

The next section will discuss the implications of these results and reflect on the

process to guide further questioning for this population and beyond.

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Chapter 6: Discussion

Introduction

This chapter discusses the aims, methodology and findings of the study, along with

an interpretation of how these results relate to the participating population. The

wider applicability of these findings will also be discussed and themes for further

research in this area suggested.

Critique of Background

The aims of this section were to identify in the academic literature, some of the

factors that have been associated with burnout in residential home staff caring for

people with dementia. Research into dementia in general and care home staff in

particular, however is sparse and under-represented given the impact of these issues

on both individuals and society.

The main outcome that this survey focused on was ‘burnout’. Consideration should

be given to this as a ‘difficult to define’ concept as reflected in the varying attempts at

its measurement. Many studies have examined burnout through psychological

testing, however an objective measure remains elusive. Both ‘trait’ and ‘state’

models of burnout have been used and both have been associated with negative

health and organisational outcomes.

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Kristensen et al. (2005) have argued that the Maslach Burnout Inventory (the most

commonly used burnout measure), is made up of 3 independent elements (feelings

of ‘exhaustion’, ‘cynicism’ and ‘inefficacy’) and that these have differing antecedents

and consequences and do not measure the concept directly. In developing their

model of burnout (the Copenhagen Burnout Inventory), Kristensen et al. (2005)

used, ‘fatigue and exhaustion’ as the core concept, however this has been criticised

as being too narrow a focus.

In reviewing factors implicated in burnout from the academic literature, results were

frequently inconsistent or only indirectly applicable to this study population. Themes

relating to these burnout factors were incorporated into a theoretical model, and the

review highlighted a number of instruments that were suitable for use in the survey.

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Critique of Methodology

The design of the study was a cross-sectional survey, using an anonymous postal

questionnaire, analysed through quantitative statistical methods. This methodology

has frequently been used to explore concepts in populations but it can also be

criticised for typically poor response rates. The cross-sectional nature also limits the

causal attributions of any significant associations.

Identifying information was not collected in the survey, possibly improving response

rates, but rendering follow-up and linkage of responses across time impossible.

Sufficient demographic information was requested, however, such that participants

might feel at risk of breach of confidentiality and exposure of responses, possibly

reducing response rates.

Collecting personally identifying information to follow participants over time, may in

this instance, have proved a greater incentive for response, providing a significantly

greater research benefit for comparatively little increased risk or burden.

The survey was part of a wider project (the ‘Enhanced Dementia Care (EDC)

Project’, run by Cardiff Council and funded by a Welsh Assembly Government grant)

evaluating staff in dementia registered residential homes in Cardiff. The population

was pre-selected and limited by the number and size of the eligible homes in Cardiff

alone. The survey methodology therefore represents a satisfactory compromise

between the needs of the EDC Project and those of this exploration of burnout.

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The theory-based burnout model benefited from having a good selection of

demographic, psychosocial, attitudinal and knowledge-based questions, however

was not exhaustive. It lacked items specifically asking about mental health and

personality, both suggested in the literature as important (and potentially

confounding) variables. The model also lacked measures to capture the

organisational and environmental characteristics of the residential homes, including

the leadership style of senior staff.

Further assessment of the care homes, including objective measurements of care

staff behaviour, whilst desirable were impracticable due to issues of expense, time

and burden. The characteristics of the care homes were also changing rapidly over

the time of the survey, with potential issues of staff turnover and recruitment and

additional homes becoming registered for dementia care (and therefore becoming

eligible for inclusion, as demonstrated by the inclusion of ‘Phase 2’ care homes)

Another potential factor affecting response to the survey was the difference in

engagement with the care homes prior to issuing the questionnaire. This was also a

contributing feature of the EDC project, with some homes having attended a number

of meetings or additional training sessions prior to this survey, including collaboration

on the survey design. The method of survey dissemination may also have an

influence as the questionnaires were distributed through the care home managers.

Encouragement for staff to complete the survey would therefore differ between

homes, as would the staff response. No further prompts or reminders were issued

following distribution of the questionnaires. This process was followed due to

pragmatic reasons, however may have contributed to reduced response rates or

skewed the demographic/altered responses on the survey instruments.

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Copenhagen Burnout Inventory (CBI)

The Copenhagen Burnout Inventory was designed by Kristensen et al. (2005),

through surveys of Danish workers, 40% of whom worked in institutions caring for

people with health needs, suggesting a comparable population.

Validated psychometric instruments to measure burnout are limited in number with

the CBI being selected as being desirable due to its 3 level construction that

addressed components of burnout based on the participant’s response to residents

as well as the work environment and more generalised stressors. It was felt that this

would result in an analysis that could differentiate the stress from working with

people with dementia from more generic work stress.

The CBI however, has been criticised for obscuring the difference between the

burnout concept and ‘simple’ fatigue (Schaufeli and Taris 2005). Also, whilst it is

based on a sound theoretical model, the CBI lacks validation through factor analysis.

Despite reservations with regard to all burnout measures, the CBI was used as a

suitable instrument with which to estimate burnout in this population.

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Dementia Knowledge Questionnaire (DKQ)

The Dementia Knowledge Questionnaire was created by Graham et al. (1997), using

a population of informal carers in the U.K. The original research divided the 19 item

DKQ into 2 main components that corresponded to ‘Irrational Beliefs’ about dementia

and ‘General Knowledge’ of dementia, comprised of 3 and 16 items, respectively.

This division was achieved through examining correlations of carer responses, rather

than factor analysis and so the suitability for the DKQ to be used in this manner was

unconfirmed. The DKQ was also designed for informal carers, potentially limiting its

applicability, however it was felt to be a useful, short and acceptable instrument for

use in this context.

Approaches to Dementia Questionnaire (ADQ)

The Approaches to Dementia Questionnaire was created by Lintern (2001). It was

designed for staff working with people with dementia in care homes, therefore was

directly applicable to this survey population. The 2 original subscales of ‘Hope’ and

‘Personhood’ were derived through factor analysis of responses from dementia care

home staff and so were considered valid for the survey population. There is little

academic literature on the use of the ADQ to study burnout, however and this might

question its applicability in this context.

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Copenhagen Psychosocial Questionnaire II (COPSOQ)

The Copenhagen Psychosocial Questionnaire was created by Kristensen et al.

(2002), using a generalised sample of the population in Denmark, across numerous

professions, including care staff working in residential and nursing homes.

The COPSOQ (Short Version) is comprised of 40 questions covering 23 dimensions,

however much of the published research has used the long version of the

questionnaire. The broad categories and many of the questions within the long and

short versions are identical and represent themes that are relevant for this study.

The use of a psychometric instrument of this complexity, along with other

instruments, in a survey of this size would lack the necessary power to have

confidence in the resulting outcomes, increasing the risk of a ‘Type I’ statistical error.

Factor analysis therefore provides an opportunity to reduce the dimensions to a

selection that can be both interpretable and reliably explain part of the variance in

responses.

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Demographic Information

The demographic information requested on the questionnaire was a selection of

variables noted to be of interest from previous studies on burnout, during the

literature review. The large number (over 18) of these questions may be criticised as

being excessive, however the design of the study as an ‘exploration’ lends itself to

the inclusion of potentially non-significant variables.

Some questions may have been subject to unanticipated misinterpretation, resulting

in erroneous results; for example, the question on ‘number and ages of children’ was

repeatedly completed in an ambiguous fashion, suggesting the fault may lie with the

question itself (See Appendix I.). Questions that were not included, but might have

been useful were job title and characteristics of the work environment, however

these details may have been too intrusive in terms of participant identification.

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Data Analysis

The choice of non-parametric statistical methods (Mann-Whitney U and Kruskall-

Wallis) for analysing associations, were appropriate given the non-normal distribution

of the CBI factors. Use of a logistic regression model, rather than parametric

alternatives, was also as a result of the non-normal distribution.

The CBI ‘Physical and Emotional Burnout’ factor most closely approximated normal

distribution and a greater number of participants might have demonstrated this,

enabling a multivariate linear regression model to be used. This model results in a

more accurate analysis of variance and would provide an improved model of

burnout.

The graphical representation of the factors in the regression model were used to aid

exploration of the factors only and were not statistically analysed and therefore

caution should be used before drawing conclusions from the observed patterns.

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Critique of Results

The response rate for the questionnaire of 31% (range: 6% to 75% per home) was

low but not unexpected for a survey of this nature, with academic literature frequently

reporting response rates of below 50% (Borritz et al. 2005).

Engagement with the individual care homes was identified as a factor in encouraging

response and closer follow up and reminders may have increased these rates.

Engaging with the care staff directly, rather than through the care home manager,

may also have increased response rates, however the method used was selected

due to pragmatic factors.

Some care home characteristics in Cardiff had been collected in 2008, 2 years prior

to this survey and these can be compared to explore if the survey responses are

representative of this population (See Figure 77.). The results for NVQ levels 2 or 3

were comparable, however the percentage of staff trained in dementia care showed

marked improvements in 2010. This may highlight the growing recognition of the

need for dementia care training in care home staff over this time. It also illustrates

that the exploration of characteristics of care home staff is a ‘moving target’, with

differences not only within and between care homes, but also across time.

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The response rates and actual numbers of responses for individual homes were

insufficient to provide enough data to enable comparisons between homes. Further

research exploring care home staff would benefit from a strategy to maximise the

number of responders.

Staff that did complete and return the questionnaires did not appear to have difficulty

in following the instructions and for the majority of responders were fully completed.

This indicates that the questionnaires were of a suitable complexity for this

population and were not too burdensome.

Most care staff were able and willing to disclose the requested demographic

information, although some questions received a low response rate, possibly due to

ambiguity in the interpretation of the question (e.g. number of children), non-

applicability (e.g. NVQ level working towards) or being visually unobtrusive (e.g.

Welsh Nationality).

The self-reported ethnicity/nationality of care staff was skewed towards a

predominantly British population, outnumbering the other nationalities at a ratio of

2:1 even when combined. This division for analysis was therefore based on

pragmatic, rather than theoretical factors and subsequent interpretation of the results

should take this into consideration. Further investigation of ethnicity as a factor in

care home research should involve a rigorous dissection of potential confounding

factors to avoid misinterpretation of findings.

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Burnout as Determined by CBI

The CBI has been used in this survey as the dependent variable to measure

‘burnout’. It has been described as having ‘fatigue and exhaustion’ as its central

concept, and this has been variously viewed as its strength and weakness

(Kristensen et al. 2005; Schaufeli and Taris 2005). The literature does recognise

associations between the CBI and sickness frequency and duration, however the

construction of ‘burnout’ as a distinct entity remains elusive (Borritz 2006). For the

purposes of this survey, using the CBI as a proxy for ‘burnout’ is appropriate

although has limitations, as with all contemporary instruments.

The original research divided the 19 item CBI into 3 components that corresponded

to ‘Personal’, ‘Work-Related’ and ‘Client-Related' Burnout with a roughly equal

number of items in each category. The aim of this division was to produce 3

separate measures to explore burnout that could account for people that work in

different occupations or that do not have traditional models of employment. The

burnout sores derived from the original structure were initially calculated, however

the CBI items were later subject to exploratory factor analysis for the purpose of this

exploration.

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CBI Descriptives and Factor Analysis

As noted in the academic literature, the response patterns for the CBI were skewed

towards positive (low burnout) responses and therefore demonstrated non-normal

distributions (Kristensen et al. 2005). Responses on the CBI scales for the survey

population were comparable to those for ‘Assistant Nurses’ in the literature for

‘Personal’ and ‘Work-Related’ Burnout, however the scores for ‘Client-Related’

Burnout were markedly reduced (See Figure 78.). This was an unexpected finding,

given the superficial similarities between the occupations of nursing assistants and

dementia care staff, however was a consistent finding across most of the 18

surveyed care homes and therefore should be considered valid.

It is plausible, that despite the intensive and demanding work by care staff in the

service of residents with complex needs, that staff do not attribute ‘working with

residents’ as being a source of their stress. The experience of stress for staff in

response to residents therefore warrants further exploration, however given that the

greatest degree of positive skew was found in ‘Client-Related’ Burnout, an

instrument with less of a ‘floor effect’ (i.e. a more normalised distribution of

responses) would be of benefit.

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The ‘Client-Related’ Burnout scale was found, through exploratory factor analysis, to

be a valid construct with all 6 items found to contribute to the CBI factor. The

‘Personal’ Burnout scale increased to 9 items and the ‘Work-Related’ Burnout scale

was reduced to 3 items in their corresponding CBI factors. These changes reduced

the correlations between the old and new ‘Personal’ and ‘Work-Related’ Burnout

scales, suggesting that the new factors were describing more independent

underlying concepts than the original factors. The correlations were still in a range

that suggested a significant degree of overlap of the underlying burnout constructs,

however and were in-keeping with the academic literature.

The 3 new factors, labelled ‘Physical and Emotional Burnout’, ‘Resident Burnout’ and

‘Work Burnout’ demonstrated good internal reliability on Cronbach’s alpha, for use as

independent scales in further analysis.

It is notable that the ‘work-related’ questions included in ‘Work Burnout’ when

compared to the ‘work-related’ questions included in ‘Physical and Emotional

Burnout’ were more strongly associating ‘burnout’ during work as opposed to

‘burnout’ because of work (See Figure 8.). The category, ‘Work Burnout’ therefore

suggests a greater attribution of burnout to the work environment, potentially

explaining the differences in response to these items.

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Forcing the components into a 2-factor model was considered and could be

achieved by combining most of the extra items into factor 1 and retaining the

‘Resident Burnout’ factor intact. This 2 factor model was not followed, however, as

the 3 factor model provided a greater explanation of the variance of responses

(68.3% versus 60.8%) and reduced correlations between factors, suggesting

additional insights into the dimensions of burnout in this population.

The analysis of the items showed that the underlying concept of ‘Physical and

Emotional Burnout’ was shared between the original categories of ‘Personal’ and

‘Work-Related’ Burnout. 3 of the questions in the original ‘Work-Related’ Burnout

dimension, however, did describe a concept independently of both the ‘Physical and

Emotional Burnout’ and ‘Resident Burnout’ categories.

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Covariate Descriptives and Factor Analysis

Dementia attitude scores for total and original subscales (‘Hope’ and ‘Personhood’)

of the Approaches to Dementia Questionnaire were comparable between this

population and those used to validate the questionnaire. The derived factors from

the ADQ items were also very similar in structure, providing further evidence that the

questionnaire was an appropriate and valid instrument to use and response patterns

were in-keeping with previous research.

Scores on the Dementia Knowledge Questionnaire, with subscales as described in

the literature, for the care home staff were comparable to those informal carers in

contact with an Alzheimer’s support group. The care home staff scored higher

overall and on subscales than other informal carer groups.

Factor analysis resulted in a very different structure to the DKQ than that described

in the literature and this was understandable given the differing process of producing

the scales.

The scores for the Copenhagen Psychosocial Questionnaire for this population,

based on the original 23 dimensions, were not compared to those found in the

literature, due to the increased risks of statistical ‘Type I’ errors (i.e. false positive

comparisons).

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The presence of the COPSOQ ‘Offensive Behaviours’ were noted however, with a

markedly higher prevalence in the study population than that from the original

research (See Figure 79.). This confirms the impression that care staff working in

dementia registered residential homes have exposure to ‘Offensive Behaviours’ that

is considerably above that expected for most occupations.

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Demographic Associations

Non-parametric analysis was completed for the new CBI factors, according to the

recoded demographic variables. The outcomes were that;

‘Physical and Emotional Burnout’ was significantly associated with ‘Time in

Current Job’ (+ve association) and ‘Time in Profession’ (+ve).

‘Work Burnout’ was also significantly associated with ‘Time in Current Job’

(+ve) and ‘Time in Profession’ (+ve).

‘Resident Burnout’ was significantly associated with ‘Age of Leaving Formal

Education’ (+ve), ‘Time in Current Job’ (+ve), ‘Welsh Nationality’ (-ve). ‘British

Ethnicity/Nationality’ (-ve) and Dementia Training (+ve for in-house training).

These differing associations further support the view that the 3 derived factors are

describing different concepts that have different associations with the demographic

variables of this population. ‘Time in Current Job’ was a consistent association

across all 3 scales, however, suggesting that the general concept of burnout may be

more strongly related to duration exposed to a specific work environment, rather than

the occupation itself (i.e. ‘Time in Profession’).

Many of the demographic factors predicted to be significantly associated with

burnout from the academic literature were not shown to have this association in this

survey. These included age, gender, relationship status, general training, socio-

economic status (using ‘NVQ Level’ as a proxy) and working hours.

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Demographic factors that were predicted to have associations with burnout and

could be viewed as having ‘connections’ to the above significant factors were

‘working for less than 2 years’ (‘Time in Job’ as a proxy), ‘non-white care staff’

(‘Ethnicity/Nationality’ as a proxy) and ‘advanced education’ (‘Age at Leaving Formal

Education’ as a proxy).

Demographic factors, predicted to be associated with burnout from the literature, that

were not explored in this survey were work-life balance conflict, health-related

lifestyle (smoking, alcohol, exercise and weight), illness and pay.

Covariate Associations

Non-parametric analysis was also completed for the derived covariate factors,

according to the 3 CBI factors, pragmatically divided into ‘High’ or ‘Low’ scores.

High ‘Physical and Emotional Burnout’ was significantly correlated with ADQ ‘Hope

Factor’ (-ve association), COPSOQ ‘Stress Factor’ (+ve), COPSOQ ‘Culture Factor’

(-ve) and COPSOQ ‘Professional Factor’ (-ve).

High ‘Work Burnout’ was significantly associated with ‘COPSOQ Stress Factor’ (+ve),

COPSOQ ‘Culture Factor’ (-ve) and COPSOQ ‘Professional Factor’ (-ve).

High ‘Resident Burnout’ was significantly associated with ADQ ‘Personhood Factor’

(-ve), COPSOQ ‘Stress Factor’ (+ve), COPSOQ ‘Culture Factor’ (-ve) and COPSOQ

‘Professional Factor’ (-ve).

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Covariate factors anticipated to be associated with burnout from the academic

literature, that were not shown to have this association included the ‘dementia

knowledge’ scales from the DKQ.

Covariate factors that were predicted to have associations with burnout and could be

viewed as having ‘connections’ to the above were hopeful attitudes (ADQ ‘Hope

Factor’ as a proxy) and ‘meaning at work’ (COPSOQ ‘Professional Factor’ as a

proxy). ‘Professional Factor’ was decided on as a label for the COPSOQ factor,

however ‘Meaningfulness’ (of work) may also have been appropriate, but overlooks

the items from COPSOQ termed, ‘possibilities for development’.

Covariate factors, predicted to be associated with burnout from the literature, that

were not explored in this survey were personality (particularly ‘neurotic’ traits) and

mental health (particularly depression).

Other psychosocial characteristics that have been associated with burnout but not

specifically explored here include; organisational commitment, social capital, justice,

job satisfaction, involvement in decisions, autonomy and leadership style (although

the COPSOQ contains elements that are likely to be correlated with these concepts).

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Offensive Behaviour Associations

Offensive behaviour is associated with workplace stress and is suggested as a factor

influencing burnout. Behaviours relating to this (‘Sexually Inappropriate’, ‘Bullying’,

‘Threats of Violence’ and ‘Physical Violence’) in the COPSOQ did not form part of the

derived factors but were variously shown to be associated with burnout on separate

analysis.

‘Physical and Emotional Burnout’ was positively associated with ‘Sexually

Inappropriate’ behaviours, ‘Threats of Violence’ and ‘Physical Violence’. ‘Work

Burnout’ was positively associated with all 4 forms of ‘Offensive Behaviour’, and

‘Resident Burnout’ was positively associated with ‘Sexually Inappropriate’ behaviours

and ‘Bullying’.

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Explorations Using Logistic Regression

Binary logistic regression was completed for the 3 CBI factors, by comparing the half

of the responders scoring the lowest with those scoring highest on the particular CBI

scale. This method was followed due to the non-parametric response pattern for all

3 factors and was a pragmatic, rather than theoretical selection.

The associated demographic variables, covariate factors and ‘Offensive Behaviours’

were included in Backwards: Liklihood Ratio methods of regression and the least

significant items systematically removed to produce the most parsimonious model for

the data.

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Logistic Regression of ‘Physical and Emotional Burnout’

Logistic regression of ‘Physical and Emotional Burnout’ with the associated variables

produced a model that included COPSOQ ‘Stress Factor’ and ADQ ‘Hope Factor’

contributing significant (p<0.05) items and ‘Physical Violence’ and ‘Time in

Profession’ contributing marginally significant (p<0.1) items. The predictive power of

this model was 83%.

Graphically exploring CBI ‘Physical and Emotional Burnout’ and COPSOQ ‘Stress

Factor’ with trend lines for high and low ADQ ‘Hope Factor’ (Figure 67.), shows a

slightly stronger association for those with high ‘Hope Factor’ scores. The pattern is

complicated by converging trend lines, however, with high ‘hope’ having lower

‘burnout’ scores for the equivalent ‘stress’.

Examining the pattern of correlation between ‘COPSOQ Stress’ and ‘ADQ Hope’

(Figure 68.), shows an overall inverse relationship, however this becomes a positive

correlation for those responders that were classified as being in the ‘high’ ‘Physical

and Emotional Burnout’ group.

This pattern, where factors anticipated to have a moderating effect on burnout

appear to correlate with increased stress, was also observed in some of the

academic literature where ‘meaning of work’ and ‘quality of leadership’ were

associated with ‘Personal Burnout’ on CBI. The suggestion was that these factors

help to maintain employees with high levels of burnout in work longer than expected,

resulting in this paradoxical effect. This concept of highly protective factors for

employees would be of significant interest for further study.

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Figure 69. explores this relationship further, plotting ADQ ‘Hope Factor’ against CBI

‘Physical and Emotional Burnout’, with trend lines for pragmatically divided COPSOQ

‘Stress Factor’.

This shows a strong inverse correlation between ‘burnout’ and ‘hope’ for staff with

low ‘stress’, but negligible correlation for those with high ‘stress’. This may suggest

that ‘hope’ is no longer protective against burnout for highly stressed staff or that

those with low hope scores and high burnout scores do not continue in employment

and thereby skew the data.

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Logistic Regression of ‘Work Burnout’

Logistic regression of ‘Work Burnout’ with the associated variables produced a model

that included COPSOQ ‘Stress Factor’ and COPSOQ ‘Professional Factor’

contributing significant (p<0.05) items and ‘Time in profession’ contributing a

marginally significant (p=0.053) item. The predictive power of this model was 79%.

Graphically exploring CBI ‘Work Burnout’ and COPSOQ ‘Stress Factor’ with trend

lines for high and low COPSOQ ‘Professional Factor’ (Figure 70.), shows a slightly

stronger association for those with low ‘Professional Factor’ scores.

Examining the associations between COPSOQ ‘Stress Factor’ and COPSOQ

‘Professional Factor’ (Figure 71.), shows an overall inverse correlation, with this

pattern being pronounced for those responders with higher levels of ‘Work Burnout’.

A similar pattern can be seen in Figure 72.

The above suggests that the moderating effect of ‘Professional’ values may have its

most significant effects for reducing stress in those employees most at risk of

burnout. The uncertainty with regard to the direction of causality for this assertion

should be taken into consideration, however.

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Logistic Regression of ‘Resident Burnout’

Logistic regression of ‘Resident Burnout’ with the associated variables produced a

model that included COPSOQ ‘Stress Factor’, COPSOQ ‘Professional Factor’ and

‘British Ethnicity/Nationality’ contributing significant (p<0.05) items. The predictive

power of this model was 75%.

Graphically exploring CBI ‘Resident Burnout’ and COPSOQ ‘Stress Factor’ with trend

lines for high and low COPSOQ ‘Professional Factor’ (Figure 73.), shows a stronger

association for those with low ‘Professional Factor’ scores.

Examining the associations between COPSOQ ‘Stress Factor’ and COPSOQ

‘Professional Factor’ (Figure 74.), shows an overall inverse correlation, with this

pattern being greater for those responders with higher levels of ‘Resident Burnout’.

A similar pattern can be seen in Figure 75.

The above suggests that the moderating effect of ‘Professional’ themes may again

have its most significant effects for reducing stress in those employees most at risk

of burnout. The uncertainty with regard to the direction of causality for this assertion

should also be taken into consideration, however.

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These charts do not account for the demographic variable, ‘British

Ethnicity/Nationality’, however. The categories were divided into ‘British’ or ‘Other’

due to the size of the ‘British’ category being more than double the size of all other

groups combined. This pragmatic step makes a gross generalization with regard to

characteristics of responders that were categorized as ‘Non-British’, however

dividing into smaller groups loses significance in statistical analysis.

Figure 76. compares CBI ‘Resident Burnout’ and COPSOQ ‘Stress Factor’ with trend

lines for ‘British’ or ‘Other Ethnicity/Nationality’ and shows a markedly stronger

association for those classed as having ‘Other Ethnicity/Nationality’.

Caution should be used when using a factor such as ‘ethnicity’ or ‘nationality’ to

directly compare measures of burnout, however as there may be cultural differences

in their perceptions and/or expression of stress (Liu et al. 2007). Given the diverse

ethnic backgrounds of the care home staff, these associations are likely to involve a

complex array of variables, and it would be a useful area for further research,

ensuring that clear objectives were set out for this topic.

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Predicting Burnout

A frequent use of binary logistic regression analysis is in producing factorial models

that can be used to predict a certain outcome e.g. ‘low’ or ‘high’ burnout on the 3 CBI

scales. These results were not included in this discussion due to the questionable

validity and implications of such values. The 3 CBI scales were created as a highly

specific reflection of this population and therefore lacks evidence as to the predictive

value of having ‘low’ or ‘high’ burnout.

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Discussion

Limitations of the Survey

The design of this exploration was a cross-sectional survey and as such takes a

‘snapshot’ at a specific time point without attempting to provide explanation or infer

causality. It also does not capture the ongoing changes in these organisations at a

time when care homes in Cardiff have been adapting to new challenges and

population demands.

The response rates to the survey were highly variable and more attention could have

been given to engaging staff directly, rather than via care home managers and also

in issuing reminders to all care homes. The responders may also have suffered from

selection bias in positively skewing the data, as those most engaged in the EDC

project may have been more likely to both respond to the questionnaire and have a

more optimistic outlook. This is an example of ‘common method variance’, which

acknowledges that confounders (such as negative outlook) can influence individual

responders to answer questions in a stereotypical manner. Also factors correlated

with burnout (e.g. exhaustion) may reduce responses from these groups or those

with high levels of burnout may have already left the workplace.

A greater response rate and therefore sample size may have enabled comparisons

between individual care homes, providing useful data on associations with burnout

and enabling greater correlation with environmental variables. Objective measures

of care would also have been of value in comparing levels of burnout with the

provision and quality of care. Qualitative methods could have been used to gain a

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more subjective view of the issues relating to burnout and although the questionnaire

did have space for additional comments, this was only used by 5 responders.

The origins of this study (as part of the ‘Enhanced Dementia Care (EDC) Project’)

have also influenced the choice of survey population, the timeframe and the choice

of variables explored.

The survey design would have been more robust with a definitive hypothesis to test,

rather than as an exploration. The statistical analysis would therefore have used

techniques that assessed those assumptions in this population, rather than using the

data to produce a model of ‘best fit’. This analysis is therefore highly specific to this

population at this particular time and caution should be used when using this data to

make more generalised assumptions about similar populations.

Another difficulty in exploring burnout in organizations relates to the chronological

correlation of factors, given that even prospective studies tend to examine individuals

that already have significant exposure to the predicted stressors. Controlling for

baseline burnout has been criticised for this, as employees have potentially already

been exposed to the stressors and may have developed features of burnout as a

result with this effect being removed from the analysis (Borritz et al. 2005).

Without controlling for baseline burnout, more psychosocial variables reach the level

of significance but assumes that the factor had its greatest impact on burnout early

on in the individual’s work-life (Borritz et al. 2005). The exploration of burnout may

therefore require analysis across the entire course of individuals’ working lives to

decipher the interplay of psychosocial variables on burnout.

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Strengths of this Survey

A strength of this study was the backing from the EDC project, without which the

survey would not have been possible. The EDC project was central in providing

opportunities to engage with the care home managers to authorise and distribute the

survey. The study attempted to provide a comprehensive overview of all care staff

that were employed in dementia-registered residential homes in Cardiff, therefore

this sample could be considered representative of this population at that time.

The number of variables under examination for this population was appropriate and

response rates were comparable with previous literature suggesting that it was not

excessively burdensome. Collection of demographic factors enabled further analysis

of the covariates and assisted in multivariate analysis of the data.

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Opportunities for Further Research

This survey has contributed to research in this population, an area that has a notable

lack of attention given its importance for current and future care of people with

dementia. A productive and healthy workforce is vital for providing these much

needed services and this survey shows that while there are significant levels of

burnout and stress that need addressing, there are also factors that have a

modifying effect and may even enable staff with high levels of stress to continue

working. Comparing these factors between individuals provides hope that

appropriately targeted interventions may change burnout for the better.

Implications for this Data

The data collected through this project has been analysed through exploratory

statistical methods. This analysis has not been exhaustive, however and further

analysis may provide additional insights into this population. The ‘goodness of fit’ of

the covariate models produced through exploratory factor analysis could be

assessed through ‘confirmatory factor analysis’ and burnout patterns could be further

assessed using ‘cluster analysis’. Exploring the covariates further as dependent

variables may also provide interesting results for this population in general and

specifically for exploring those factors found to have associations with burnout.

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Implications for this Population

This survey has provided an insight into burnout in care staff in Cardiff dementia-

registered residential homes and could serve as a pilot to further examine this

population, with a greater emphasis on improving response rates. Future studies

could continue to assess burnout across time, through longitudinal studies, linking

responses of individuals to assess change and infer causality.

Other elements that could be explored would involve attempts to further sub-divide

the covariate factors or include additional instruments to explore the components

within the factors that are most significant, particularly amongst the ‘Stress’-related

items.

This survey could also influence future investigations, by suggesting a reduced

number of demographic variables and covariates to refine the burnout models.

Other factors that would be valuable to include in analysis would be individual

personality and mental health factors as well as additional organisational factors for

the care homes.

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Implications for Research Theme

Research into dementia care staff in care homes is under-represented and worthy of

further analysis. There are significant opportunities in this field to have more in-

depth analysis of specific populations, to expand analysis to more care homes

geographically or to include all staff within the homes e.g. administrative or domestic

staff. Expanding investigations to include other populations, outside of the dementia

care home sphere may provide more generalizable results, however may lose focus

on the improving the care of people with dementia, which was the starting point for

this survey and the EDC project.

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Discussion Summary

This discussion has commented on the strengths and weaknesses of performing a

survey on this population and detailed some of the challenges that face further

research in this area. A critical analysis of the theoretical background to the study

has outlined the key factors that are thought to influence burnout, an often nebulous

concept. The theoretical and practical limitations have been discussed to explain the

choices made in following the methodology as described.

The results and outcomes of the data analysis have been examined to try to

understand the stress that faces care staff working in this demanding profession.

The potential for interventions to improve levels of burnout has been noted and some

of the key variables influencing this concept have been discussed. The case for

prioritising much-needed research has been raised and, given the projected

demands for care homes in the future, this would appear to be a worthwhile venture.

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Chapter 7: Conclusion

Background

Caring for the ageing population is resulting in more people living in residential

accommodation with approximately 40% having dementia-related needs (BGS

2011). Care staff in residential homes are at high risk of work related strain and

burnout, potentially leading to poor health and care outcomes for both staff and

residents (Schmidt and Diestel 2013; Bishop et al. 2008).

The academic literature identifies a number of individual and organisational elements

that have been shown to correlate with or predict outcomes from burnout.

Methodology

The aim of this project was to explore selected factors that are associated with

burnout in populations of care staff working in dementia-registered residential homes

in Cardiff.

This was achieved through the analysis of anonymous questionnaires, using

psychometric instruments validated as providing reliable data in comparable

populations.

The instruments were chosen to balance a robust exploration of the chosen factors

with minimal burden and as such required a necessary compromise in the elements

under examination.

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A key focus for the survey was related to the highly specific occupation that this

population was comprised of i.e. care staff in residential homes looking after people

living with dementia. The survey therefore included questions on dementia

knowledge and attitudes as well as more general questions on psychosocial

elements.

Results: Descriptives

Comparing the Copenhagen Burnout Inventory (CBI) scores (using the original 3

structures) for this population with the original research showed that scores for

‘Personal’ and ‘Work-Related’ Burnout were comparable. Scores for ‘Client-Related’

burnout were substantially lower for the study population in this survey. The reason

for this is uncertain. Results for the covariate measures were also comparable,

although high exposure to ‘Offensive Behaviours’ was noted.

Results: Exploration

Exploring the Copenhagen Burnout Inventory (CBI) in the Cardiff care staff

population showed that 3 broad concepts emerged; a general stress component

labelled ‘Physical and Emotional Burnout’, a work stress component, ‘Work Burnout’

and a resident specific component, ‘Resident Burnout’. Of note, only the ‘Resident

Burnout’ concept matched the classification as intended by the authors of the CBI.

The covariate instruments were explored in a similar manner. Dementia knowledge

could be divided into 2 factors, however no obvious pattern in the descriptions of the

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questions could be determined. The factors were used in further analysis based on

an assumption that they represented a division in the difficulty of the questions.

The division of dementia attitudes into 2 concepts largely matched the components

described in the academic literature. Very strong correlations between the factor

scores and those based on the literature were also described, further demonstrating

the usefulness of the Approaches to Dementia Questionnaire for this population.

Psychosocial factors were shown to have 3 core elements on exploratory analysis.

These elements were labelled, ‘Culture Factor’, ‘Stress Factor’ and ‘Professional

Factor’. The COPSOQ (short version) describes 23 ‘dimensions’ made up from

combinations of the original 40 items, however the sample size of the Cardiff care

staff population used was deemed insufficient to power an analysis using this

number of variables. The 3 factors were therefore made up of 29 items that showed

a reasonable loading (>0.4) on the factors.

4 of the 11 COPSOQ items discarded in this process included those related to

‘Offensive Behaviour’, variables highlighted in the academic literature as having

significant impacts on burnout and present in up to 40% of this population. These

incidents were felt to be worthy of further investigation and were included in analysis,

although the less frequent behaviours were not included in the models due to low

numbers.

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Results: Regression

Further analysis of burnout was through dividing the survey population according to

those scoring in the upper or lower range of values on the outcome variables (the 3

derived CBI factors). Each variable underwent logistic regression using associated

demographic information, covariates and ‘Offensive Behaviours’ to produce the most

parsimonious model to explain the variance.

‘Physical and Emotional Burnout’ was found to be positively associated with ‘Stress’

and negatively with ‘Hopeful’ attitudes towards people with dementia. Having less

experience in the profession (<5 years) and being exposed to physical violence were

also predictive variables for ‘Physical and Emotional Burnout’.

‘Work Burnout’ was also found to be positively associated with ‘Stress’ and

negatively with ‘Professional’ values. Also of note was that having less experience in

the profession (<5 years) was also a predictive variable for ‘Work Burnout’.

‘Resident Burnout’ was again found to be positively associated with ‘Stress’ and

negatively with ‘Professional’ values. Also of note here, was that being ‘British’ was

negatively associated with ‘Resident Burnout’.

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Of the 3 regression models, the variables explaining ‘Physical and Emotional

Burnout’ had the greatest predictive power followed by ‘Work Burnout’, then

‘Resident Burnout’.

This pattern was noted through the ‘Nagelkerke R Square’ score (approximate

variances of 62%, 44% and 39%, respectively), as well as the percentage scores

correctly predicted (83%, 79% and 75%, respectively). The ‘Hosmer and Lemeshow

Test’ demonstrated that the models were a good fit for the data and the ‘Collinearity

Statistic’ indicated that none of the variables in the models were disproportionately

influential.

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Discussion of Burnout

Burnout on all 3 of the components, identified through factor analysis of the CBI, was

significantly associated with physical and emotional ‘Stress’. This finding is in

keeping with the literature on burnout, where the concept has been variously termed

‘strain’, ‘exhaustion’ or ‘fatigue’ (Kristensen et al. 2005; Masalach 2003; Borgogni et

al. 2012). Current opinion on burnout also recognises the influence of modifying

factors and each of the CBI components demonstrated this phenomenon.

‘Physical and Emotional Burnout’ was reduced through ‘Hopeful’ attitudes on the

Approaches to Dementia Questionnaire (ADQ) and may broadly correlate with

engagement (cynicism having been negatively associated with this) (Maslach 2011;

Schaufeli and Salanova 2011). Increasing ‘Hope’ was also, counter-intuitively

associated with higher levels of burnout and stress. This may be explained in some

cases, by staff continuing to work despite high levels of burnout due to the protective

effects of ‘hope’, as described with other positive psychological states (Borritz et al.

2005; Clausen 2009). It is also uncertain if these associations result from an

individual’s generally ‘hopeful’ attitude or is specific to attitudes towards people living

with dementia only and the effects limited to this particular survey population.

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Both ‘Work Burnout’ and ‘Resident Burnout’ were reduced through positive

‘Professional’ values on the Copenhagen Psychosocial Questionnaire II (COPSOQ).

This corresponds to the positive effects of ‘meaning of work’, ‘commitment to the

workplace’ and ‘possibilities for development’ (Bishop et al. 2008; Borritz 2006;

Clausen 2009; Taris et al. 2002). Interestingly, the demographic variable, ‘Time in

Profession’ (< 5 years) was positively associated with both ‘Physical and Emotional

Burnout’ and ‘Work Burnout’ in the regression model but negatively in the

demographic associations. Greater experience has variously been described in the

literature as suggesting increased or reduced burnout (Brodaty et al 2003;

Zimmerman et al. 2005).

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Implications for Further Research

This survey has demonstrated, through exploratory analysis, that the CBI can be

divided into 3 subscales or ‘facets’ of burnout that may help to explain this complex

phenomenon. The main finding of regression analysis was that the items comprising

the ‘Stress’ factor of the COPSOQ were the dominating factors associated with

burnout in each of the 3 facets explored.

This strong association with burnout, however was in each case mediated by

alternate associations (although noting that commentary on the direction of causality

is not supported by evidence from this study). These mediating ‘forces’ differed in

their composition between the 3 facets of burnout, giving further weight to the

suggestion that the ‘facets’ represent valid constructs and have the potential for

understanding burnout in the ‘real world’. It is notable that ‘Stress’ was the most

common and significant of the associations with all burnout scores, but that there

were differing modifying factors, indicating an exploration of ‘positive psychology’

influences may be beneficial for future research (Meyers et al. 2012).

These elements in the burnout models remain insufficient to fully explain the concept

of burnout in this population, however. There are a number of individual factors

highlighted by the academic literature that were not included in the model, such as

mental health and personality, and these may prove valuable in explaining these

concepts further. There are also likely to be a number of organizational and/or

environmental factors contributing to burnout, such as leadership style, present

across the care home sector, and these would benefit from further study also.

145

A further concept worthy of deeper exploration is that of the individual components of

the ‘Stress Factor’, to ascertain which items are of particular relevance for each of

the 3 burnout subscales.

The practical applications from exploring these concepts further may be through

workforce management or designing and implementing interventions. Research

may also have use in identifying individuals or care homes that have ‘outlying’

burnout characteristics (positive or negative) for guiding interventions and monitoring

change.

Ensuring a resilient and professional workforce to care for people with dementia in

residential homes is both necessary and desirable. Burnout brings with it

disadvantages in terms of cost and quality of resident care.

This exploration has re-iterated the themes from previous research that burnout is

highly linked with physical and emotional exhaustion and can, to a certain degree be

modified in staff members through hope and professionalism. Other elements

implicated in burnout from this study have been the length of time in profession,

exposure to physical violence and non-British nationality and all of these areas would

benefit from further exploration (see Figure 80.).

146

147

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163

Appendix I

Questionnaire Information Leaflet

Questionnaire Booklet

164

Questionnaire Information Leaflet

165

166

167

Questionnaire Booklet

168

169

170

171

172

173

174

175

176

177

178

179

180

181

182

183

184

185

186

187

Appendix II

Recoding Demographic Variables

Care Homes

Sex

Age

Marital Status

Children

Education

NVQ Level

Dementia Training

Job Status

Time in Job

Time in Profession

Shift Pattern

Hours Worked

Nationality/Ethnicity

188

Recoding Demographic Variables

In order to make best statistical use of the demographic variables, a pragmatic

approach to analysis was followed. The individual variables were examined and

separate groups clustered into the optimal number of items within each variable.

This number was typically 2 or 3 groups and was stratified in order to accommodate

the maximum number of responses in each grouping. This was to enable greater

statistical power when using the variables in multivariate analysis.

189

Care Homes

The division of care homes between ‘Phase 1’ (n=95) and ‘Phase 2’ (n=68) was kept

for the analysis. This was done on the assumption that the care homes in ‘Phase 1’

might be expected to have greater experience of dementia care, given that they had

been registered for dementia care for longer and a greater number of their key staff

had received additional training as part of the ‘EDC Project’ prior to the survey.

Care Home Frequency Percent

Phase 1 95 58.3

Phase 2 68 41.7

Sex

The divisions of sex between male (n=25) and female (n=118) were kept for the

analysis as they already represented a dichotomous variable.

Sex Frequency Percent

Female 118 72.4

Male 25 15.3

190

Age

The classification of age was stratified above or below 44 years, with 92 participants

aged 44 years and below and 62 participants aged above 44 years.

Age Frequency Percent

Under 44 92 56.4

Over 44 62 38.0

Age Frequency Percent

16-20 8 4.9

21-29 39 23.9

30-44 46 28.2

45-59 51 31.3

60+ 10 6.1

No Response 9 5.5

191

Marital Status

Marital status was classified as either attached (married or in a long term

relationship) (n=93) versus single (never married, divorced or widowed) (n=62)

status.

Marital Status Frequency Percent

Attached 93 57.1

Not attached 62 38.0

Marital Status Frequency Percent

Long Term Relationship 39 23.9

Married 55 33.7

Single, Divorced 21 12.9

Single, Never Married 33 20.2

Single, Widowed 7 4.3

No Response 8 4.9

192

Children

Participants were stratified according to if they did (n=102) or did not (n=32) state

that they had children.

Children Frequency Percent

Yes 103 63.2

No 32 19.6

No Response 28 17.2

193

Education

Participants were stratified according to whether they had formal education up to

(n=76) or beyond (n=76) 16 years old.

Age of leaving formal

education

Frequency Percent

16 or under 76 46.7

Over 16 76 46.7

Age of leaving formal

education

Frequency Percent

<15 Years 19 11.7

15-16 Years 57 35.0

17-18 Years 30 18.4

19-21 Years 20 12.3

22+ Years 26 16.0

No Response 11 6.7

194

NVQ Level

Participants were stratified as having obtained upto (n=48) or greater (n=35) than

NVQ level 2.

Current NVQ Level Frequency Percent

2 or below 48 29.4

Over 2 35 21.5

Current NVQ Level Frequency Percent

1 4 2.5

2 44 27.0

3 24 14.7

4 4 2.5

>4 6 3.7

No Response 81 49.7

195

Participants were also stratified as working towards NVQ levels upto (n=29) or

greater (n=33) than NVQ level 2.

NVQ Level Ongoing Frequency Percent

2 or Below 29 17.8

Above 2 33 20.2

NVQ Level Ongoing Frequency Percent

1 5 3.1

2 24 14.7

3 23 14.1

4 4 2.5

>4 5 3.1

No Response 102 62.6

196

Dementia Training

Participants were stratified as having had no (n=21), internal only (n=94) or external

(n=33) dementia training.

Dementia Training Frequency Percent

None 21 12.9

In-house 94 57.7

External 33 20.2

Dementia Training Frequency Percent

None 21 12.9

In-house 95 58.3

In-house & external 17 10.4

External 10 6.1

Other Qualification 5 3.1

No Response 81 9.2

197

Job Status

The status of participants jobs were categorized as full-time (n=104) or part-time

(n=43) with permanent contracts only, as other options received few responses

(n=7).

Job Status Frequency Percent

Full-Time Permanent 104 63.8

Part-Time Permanent 43 26.4

Job Status Frequency Percent

Casual 1 0.6

Full-Time Temporary 6 3.7

Part-Time Permanent 43 26.4

Full-Time Permanent 104 63.8

Other 1 0.6

No Response 8 4.9

198

Time in Current Job

The length of time participants had been in their current job was divided into 3

categories, < 1 year (n=43), between 1 and 5 years (n=73) and 6 or more years

(n=40).

Time in Job Frequency Percent

Less than 1 year 43 26.4

Greater than 1 year 113 69.3

Time in Job Frequency Percent

Less than 1 year 43 26.4

1 to 5 years 74 45.4

6 to 10 years 19 11.7

11 to 20 years 14 8.6

20+ years 6 3.7

No Response 7 4.3

199

Time in Profession

The length of time participants had been in the profession was divided into 2

categories, 5 and under (n=74) or over 5 (n=80) years.

Time in Profession Frequency Percent

Less than 5 years 75 46.0

Over 5 years 79 48.5

Time in Profession Frequency Percent

Less than 1 year 20 12.3

1 to 5 years 55 33.7

6 to 10 years 17 10.4

11 to 20 years 33 20.2

20+ years 29 17.8

No Response 9 5.5

200

Shift Pattern

The shift patterns that participants worked were divided into 3 categories, permanent

day shifts (n= 51), permanent evening/night shifts (n=30) and other shifts (n=24)

(including rotating 8 and 12 hour shifts and ‘other’). Shift changes were categorised

as changing > weekly (n=85), < weekly (n=18) or never changing (n=32).

Shifts Frequency Percent

Permanent Day 52 31.3

Permanent Eve/Night 30 18.4

Other 24 14.7

Shifts Frequency Percent

Permanent Day 52 31.9

Permanent Eve/Night 30 18.4

Rotating 12 Hour Shifts 7 4.3

Rotating 8 Hour Shifts 6 3.7

Other 11 6.7

No Response 57 35.0

201

Shift Change Frequency Percent

More than weekly 85 52.1

Less than weekly 50 29.6

Shift Change Frequency Percent

Greater than Weekly 85 52.1

1-2 Weekly 5 3.1

3-8 Weekly 7 4.3

8+ Weekly 6 3.7

Never 32 19.6

No Response 28 17.2

202

Hours Worked

The basic hours worked by participants was stratified as above 35 hours (n=90) or

35 hours and below (n=64).

Basic Hours Worked Frequency Percent

Less than 35 64 39.3

More than 35 90 55.2

Basic Hours Worked Frequency Percent

Less than 15 Hours 5 3.1

16-25 Hours 26 16.0

26-35 Hours 33 20.2

36-44 Hours 75 46.0

Greater than 44 Hours 15 9.2

No Response 9 5.5

203

Overtime Worked Frequency Percent

Less than 5 hours 50 30.7

More than 5 hours 63 38.7

Overtime Worked Frequency Percent

Less than 5 Hours 51 3.1

5-10 Hours 41 14.7

11-15 Hours 15 14.1

16-20 Hours 3 2.5

Greater than 20 Hours 3 3.1

No Response 50 30.7

Other Job Frequency Percent

None 106 3.1

1-10 Hours 2 14.7

11-15 Hours 3 14.1

16-20 Hours 2 2.5

Greater than 20 Hours 1 3.1

No Response 49 30.1

204

Ethnicity/Nationality

Participants were initially divided according to if they considered themselves Welsh

(n=40) or not (n=15).

Welsh Nationality Frequency Percent

Yes 40 24.5

No 15 9.2

No Response 108 66.3

Additional questions on ethnicity were divided into British (n=104) or not British

(n=46), including various combinations of ‘Asian’ (n=18), ‘Afro-Caribbean’ (n=17) and

‘European’ (n=12) ethnicities.

Ethnicity Frequency Percent

British 104 63.8

Other 46 28.2

Ethnicity Frequency Percent

British 103 63.2

Asian 18 11.0

Afro-Caribbean 17 10.4

European 13 8.0

No Response/Other 12 (9/3) 7.4

205

Appendix III

COPSOQ: ‘Offensive Behaviours’ Frequency

Behaviour

Bullying: Frequency

Bullying: Protagonist

Sexually Inappropriate: Frequency

Sexually Inappropriate: Protagonist

Threats of Violence: Frequency

Threats of Violence: Protagonist

Physical Violence: Frequency

Physical Violence: Protagonist

206

COPSOQ: ‘Offensive Behaviours’ Frequency

Behaviour No A Few x Monthly Weekly Daily No

Response

Sexually

Inappropriate

150

(92%)

11 (6.7%) 0 1 (0.6%) 0 1 (0.6%)

Threats

Violence

97

(59.5%)

55

(33.7%)

2 (1.2%) 2 (1.2%) 4 (2.5%) 3 (1.8%)

Physical

Violence

100

(61.3%)

53

(32.5%)

5 (3.1) 0 3 (1.8%) 2 (1.2%)

Bullying 146

(89.6%)

12 (7.4%) 1 (0.6%) 1 (0.6%) 2 (1.2%) 1 (0.6%)

207

Behaviour

Bullying: Frequency

Bullying: Protagonist

208

Sexually Inappropriate: Frequency

Sexually Inappropriate: Protagonist

209

Threats of Violence: Frequency

Threats of Violence: Protagonist

210

Physical Violence: Frequency

Physical Violence: Protagonist