Running head: THESIS Final 1

Thesis Final

Aaron Sweazy

Concordia University of Nebraska

Applied Research in Public Health

MPH 598

Dr. Diane Neal

April 26,2015

THESIS FINAL 2

Abstract

Individuals perceived to be depressed as well as homeless were compared to see if there

was a significant relationship between the 2 cohorts. A meta-analysis was conducted to

extrapolate data to give a more precise answer to the relationship of depression and homelessness

while attempting to eliminate pure speculation on association between them. The results of the

meta-analysis showed support for the hypothesis of there being statistical significance of 50% or

more of individuals living being homeless also had depression with a .combined effect falling in

between the lower and upper confidence levels which showed no significance in there being

enough statistical data to reject the hypothesis. In this exploration of poverty’s association with

medical problems of mental health, I tested the hypothesis associated with my research question.

The null hypothesis is, Hₒ: µ≤ .50 = There is no statistically significant relationship showing less

than half the people living in poverty who have mental health issues, while the alternative

hypothesis is, Hα: µ>.50 = There is a statistically significant relationship showing less than half

the people living in poverty who have mental health issues.

Additionally I believe study design should be examined to see if any flaws existed in

reporting as there was a rather large NIa and NIc conglomeration in some of the studies, which

I feel overshadow the other studies in the meta-analysis that have fewer. I will say however, by

having a wide range of results you get a very broad range of outcomes which in turn makes me

want to dissect the study down further to see what is the main reason certain studies had a higher

follow up rate than their peers. A major issue prevalent in the studies were identifying if the

antecedent was depression causing homelessness, or homelessness causing depression. If all

studies would have been able to pinpoint the exact causation of the two cohorts it would have

been a more valuable study in terms of finding what caused the other.

THESIS FINAL 3

Thesis Draft

Introduction

Problem Statement

Individuals in lower socio-economic situations find themselves unable to seek treatment

for medical issues due to priorities such as trying to pay rent or amenities; however, by delaying

medical help, stress may ensue which in turn can result in public health issue in a given

environment (World Health Organization [WHO], n.d.). Poverty is socio-economic status as it

pertains to the variables in living conditions along with income, educational background and

employment (Iyengar, 1990, p. 19-23). Initially researchers evaluated the effects of

individualized perception of net worth on mental health, regardless of actual household income

explored regardless of how much income was attributed to a household, by taking previously

documented cases of mental health with relationships to poverty (Dombeck & Wells-Moran,

2006, p. 22). Information was classified by using variables of age and low socio-economic

status based on the standards set forth as to how to measure poverty as presented by the U.S.

Department of Commerce’s Census Bureau (2014). By taking the information available from

previous research, the idea was to infer whether or not the antecedent (poverty) had an adverse

effect on mental health. Furthermore, if the aforementioned did prove to be relevant, what

conclusions could be drawn from the research to help solidify a call to action to help circumvent

the issue of a correlation between poverty and mental health? Other mental health disparities

found to result from complications of societal pressure and poverty were also dichotomized

based on assumptions from the readings (Moser, 1996).

Purpose

THESIS DRAFT 4

Poverty is a lifestyle which continues to gain momentum in society whether individuals

want to be part of it or not. Economic dependence on government assistance and lethargic

work ethics have crippled our economy and resulted in a complacent society simply looking for

handouts. Poverty in the United States is based on income, in most of the United States it starts

off with a household of one being set at an annual income of $11,700 whereas each additional

person within a household is an extra $4,160 (The Federal Register & Department of Health and

Human Services (HHS), 2015, p. 3236 -3237). The purpose of this research was to determine if

the poverty lifestyle has an effect on the mental health diagnosis. Because, poverty was

determined to be an antecedent to medical issues associated with mental health, it has helped

pave the way to finding resolutions to eradicate poverty.

Research Questions and Associated Hypothesis

Guiding my research was the question; “Is there statistical significance of 50% or more of

individuals living in poverty having mental health issues?”

In this exploration of poverty’s association with medical problems of mental health, I

tested the hypothesis associated with my research question. The null hypothesis is, Hₒ: µ≤ .50 =

There is no statistically significant relationship showing less than half the people living in

poverty who have mental health issues, while the alternative hypothesis is, Hα: µ>.50 = There

is a statistically significant relationship showing less than half the people living in poverty who

have mental health issues.

If my hypothesis is found to be accurate, the awareness of poverty and it’s direct effect

on mental health could be taken into a more serious form and methods to find a cure for poverty

could be taken more seriously in order to in turn hopefully reduce mental health.

THESIS DRAFT 5

Potential Significance

Wellness programs could be established due to the correlation being found between

poverty and mental health. Lifestyle coaching at no cost could be provided in local

communities to help educate individuals in poverty. Specific education could help alleviate

struggles financially and in turn help to diminish the effect of mental illness. By providing a

free or sliding scale fee with educational treatment options the prevalence of poverty could

potentially decline which would allow for an independent society and growth among business

and industry.

Literature Review

Theoretical Framework

The Social Cognitive Theory (SCT) has indicated how individuals in a social setting

learn through watching and imitating those in the area around them (Denler, Wolters, & Benzon,

2014), in how it pertained to the association between poverty and mental health. An individual

that was raised in a social setting such as poverty and grew up happy, was likely to be happy

being poor as adults, on the contrary if someone grew up in an affluent household and then

moves out on their own and was surrounded by poverty, they may have adapted to living below

their means to fit in with the rest of society in a classic case of nature vs. nurture (Rowe &

Rodgers, 1997). Social support systems are significant resources in managing stressors,

supporting mental health issues (i.e.: sudden death in family) while attempting to improve the

quality of living (Unger & Wandersman, 1985).

THESIS DRAFT 6

Current Status of Mental Health/Poverty

Poverty was associated with an increased risk for psychological problems (Santiago,

Kaltman, & Miranda, 2013, February 1). Research suggested when low-income individuals do

receive evidence based mental healthcare, they responded well to treatment. A review of

treatment studies have been conducted with low-income groups and provide recommendations

for clinicians working with low-income children or adults. Low-income individuals and families

are engaged in evidence-based care saw their outcome generally be positive in nature.

Acceptance of Depression Treatments for older adults in low-income was examined

within differences of groups (Choi & Morrow-Howell, 2007, p. 423-433). Treatment Evaluation

Inventory (TEI) was conducted to see the basic mental health needs of older adults. Results were

calculated by taking mean TEI scores by treatment modality and homebound status. TEI’s were

composed by the calculations of Clinical Based Cognitive Therapy (CT), In-Home Cognitive

Bibliotherapy (CB), Antidepressant Medication (AM), and Physical Exercise Intervention (PE).

206 participants were documented with 79 being homebound status and 127 being Not

Homebound.

Potential Confounders

Meta-Analysis has a likelihood of bias due to flaws, which could be confusing. Potential

data may be skewed to inaccurately portray final results. In a mental health correlation with

poverty, determining if poverty is an antecedent to mental health is a confounding statistic that

needed to be accounted for in pre-existing mental health issues (National Institutes of Health

(NIH) & National Institute of Neurological Disorders and Stroke (NINDS), 2015). Some

participants may have the same level of poverty as another participant, but data is skewed

because Participant “A” may receive an influx of government assistance into their household,

THESIS DRAFT 7

whereas Participant “B” may have no helping resource (Muhlhausen & Tyrell, 2014).

Additional data can be skewed if participants are included in the research and make income

greater than the poverty guidelines, but are frivolous in their spending habits and live above their

means (Dividend Mantra, 2014, April 24).

Methodology

I will utilized meta-analysis study design with the idea of finding whether or not there is

a direct link connecting socio-economic status (in this case poverty), with mental health

illnesses. Meta-analysis fit for this type of research because it permitted me to save time by

bringing together multiple studies to produce an adequate sample size which in turn allowed for

a more parallel comparison to a population, which allowed me to see that a correlation between

the two actually exists. Through the utilization of Academic Search Premier’s Esbsco Host, I was

able to find articles that helped to better understand the procedural base for the methods section.

I used the terms homeless and depression while limiting studies to full text, references available,

and from the date range of 2003-2013. 6 studies were including in the final analysis with

Rosario’s A, B, and C trials (2012) making up half of the research. As indicated in Table 1 and

Figure 1, I have illustrated the method I carried out to collect the acquired studies used in the

meta-analysis.

Table 1

Inclusion and Exclusion Criteria

Inclusion Criteria Exclusion Criteria

Homeless Studies without full text reports

Depression Studies conducted after 2013 or prior to 2003

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After taking into consideration the inclusion and exclusion criteria, I recognized 20

outcomes came from scholarly reviewed peer journals available with full text reports. Based on

the information I was able to extrapolate, I would take account of the information in this meta-

analysis. The total outcomes resulting from inclusion and exclusion is offered in Figure 1.

Figure 1: Results of Inclusion and Exclusion Criteria

Data Analysis Plan

I gauged the correlation of poverty and mental health via the findings from research

which has already been done. These previously conducted studies became a collective cohort by

using a meta-analysis to increase the sample size and which permitted me to draw new

inferences from them. In my research the utilization of Nominal measurements with discrete data

was utilized to identify exposures in poverty. In addition to exposure of poverty, effects of the

measures of Mental Health and Income were used with Nominal measurements using categorical

data in finding Depression/Homeless correlation. With the information gathered, I recorded the

data contained from the remaining five studies on the exposure to poverty and the effect it

attributes to mental health by placing it into MetaEasy (Kontopantelis & Reeves, 2009) data

sheet in Excel (Microsoft, 2014).

Table 2

Studies (6)

Studies excluded (14)

Studies included (20)

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Effects and Exposures to be measured

Type of Variable (Effect or Exposure)

Description Level of Measurement

Exposure Poverty Nominal Effect

Effect

Measure of Mental Health

Measure of Income

Nominal

Nominal

Results

Table 3 MetaAnalysis Collection A

Data collectionStudy and Variable

Calculated and Wanted?

Reversed Effect? Method Selected Missing Data from method

Reardon, 2003: Depressive Symptoms

Yes No 7 lCI95(OR) and uCI95(OR)

Reardon, 2003: Bipolar Disorder

Yes No 7 lCI95(OR) and uCI95(OR)

Rosario, 2012a: Depressive & Anxious Symptoms

Yes No 6 OR and lCI95(OR) and uCI95(OR)

Rosario, 2012a: Depressive & Anxious Symptoms

Yes No 6 OR and lCI95(OR) and uCI95(OR)

Rosario 2012 b: Anxious Symptoms

Yes No 6 OR and lCI95(OR) and uCI95(OR)

Rosario, 2012b: Anxious Symptoms

Yes No 6 OR and lCI95(OR) and uCI95(OR)

Rosario 2012 c: Depressive Symptoms

Yes No 6 OR and lCI95(OR) and uCI95(OR)

Rosario, 2012c: Anxious

Yes No 6 OR and lCI95(OR) and

THESIS DRAFT 10

Symptoms uCI95(OR)

Shelton, 2006: Depressive Symptoms, Prescribed antidepressants, Psychiatric hospitalization, Suicidal Ideation

Yes No 2 NONE

Shelton, 2006: Prescribed antidepressants

Yes No 2 NONE

Shelton, 2006: Psychiatric hospitalization,

Yes No 2 NONE

Shelton, 2006: Suicidal Ideation

Yes No 2 NONE

van den Bree, 2009: Depressive Symptoms

Yes No 2 NONE

Excluding Shelton (2006) & van den Bree (2009) the studies in Meta Analysis Table 3

are missing both the lower and upper CI. Studies were complete based on the method they are:

Reardon (2003) is method 7 or Continuous just needing input of Nia and NCa and (P or (T and

DF)) (University of Manchester, n.d.). None of the studies showed revered effects, while on the

contrary, all of the studies were calculated and wanted.

Table 4 MetaAnalysis Collection B

Study and Variable

effect effecti95 Effectu95 Value for Error Bars

Count

Reardon, 2003: Depressive Symptoms

0.2745 0.111 0.4380 0.1635 1

Reardon, 2003: Bipolar disorder

0.2149 0.0514 0.3784 0.1635 2

Rosario, 2012a: Depressive Symptoms

0.4176 0.1038 0.7315 0.3139 6

Rosario, 2012a: Anxious

0.1351 -0.1781 0.4490 0.3139 7

THESIS DRAFT 11

SymptomsRosario,2012b: Depressive Symptoms

0.4176 0.1038 0.7315 0.3139 11

Rosario,2012b: Anxious Symptoms

0.4176 0.1038 0.7315 0.3139 12

Rosario, 2012c: Depressive Symptoms

0.1351 -0.1781 0.4490 0.3139 16

Rosario,2012c:Anxious Symptoms

0.1351 -0.1781 0.4490 0.3139 17

Shelton,2006:Depressive Symptoms

0.2626 0.2626 0.4404 0.1778 21

Shelton,2006: Prescribed Antidepressants

-0.0400 -0.0400 0.2079 0.2479 22

Shelton, 2006: Psychiatric Hospitalization

0.3302 0.3302 0.6190 0.2889 23

Shelton, 2006:Suicidal ideation

0.1096 0.1096 0.3039 0.1943 24

van den Bree, 2009: Depressive Symptoms

0.2990 0.2990 0.3551 0.0561 28

My interpretation of the data stemming from MetaAnalysis Table 4 is the effect is the

most important outcome of the MetaAnalysis, which would be agreed upon by several other

sources of scholarly influence (University of Manchester, n.d.).

Table 5 MetaAnalysis Collection C

THESIS DRAFT 12

Heterogeneity

measures

value df p-value

Cochrane Q 2.80 5 0.7304

tau2

estimate

(DL) 0.0000

tau2

estimate

(ML) 0.0000

tau2

estimate

(PL) 0.0000

I2 %0.00

H2M 0.0000

The P-Value of Cochrane Q measuring heterogeneity was found to be 0.7304 according

to MetaAnalysis Table 5. Due to the P-Value being larger than .05 I was able to utilize the full

effect model in my research.

Results

I used six studies in conducting a Meta-Analysis study. The combined effects (FE) was

to the right of the line of no effect, thus statistically there is significance within the meta-

analysis. Additionally there was a Combined Effect: 0.2866 with a 95% Confidence Interval

THESIS DRAFT 13

show a Lower Limit of 0.2370 and an Upper Limit of 0.3362. Because the LL and the UL

were both positive in nature and tor the right of the line of no effect, there was statistical

significance in the effect. Additionally, you cannot technically reject the null hypothesis

because the data lies within the range of the upper and lower limits.

Conclusions

Interpretation of the Findings

Figure 2. Forest Plot

It is possible that one of the disputes with this meta-analysis is in trying to define exactly

what mental health is. If at the present time there is not a stable definition for mental health and

the ailments linked to it, there may in turn be variances in the results measured. Looking at

van den Bree, 2009

Shelton, 2006

Rosario, 2012c

Rosario, 2012b

Rosario, 2012a

Reardon, 2003

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Meta1Summary, 12 of 13 studies favor the control group (NCa) based on the plots on the right

side of the line of no effect (Reid, 2006) just as they were in the Meta 1 Model. To ensure

interpretations do not exceed the data, findings, and scope the results stemming from the meta-

analysis will only be considered when analyzing if there is a correlation between homelessness

and depression.

Limitations of the Study

One of the issues with this meta-analysis is in trying to define exactly what mental health

is. If at the present time there is not a stable definition for mental health and the ailments linked

to it, there may in turn be variances in the results measured I noticed the intervention group size

after (Nia) and the control group size after (NCa) were the same in all three trials conducted by

Rosario (2012). Upon looking closer though the Nia to NCa participants increased from 167 to

1031 (Reardon, 2003), 682 to 14,206 (Shelton, 2006), and 428 to 10,005 (van de Breen, 2009),

which had me wondering the validity of reporting in participants due to the broad range from

start to finish, the results of the meta-analysis seem to agree with previous studies in supporting a

correlation between homelessness and mental health disorders. In the text by Reardon et.al.

(2003), there was an issue in defining what homelessness meant as it seems to be a very broad

term with some aspects of homelessness being truly without a home, and some being just

displaced. Limitations furthermore existed with the failure to identify at what point in time

homelessness occurred and if it was a lingering problem or a singular time of struggle. There is

confusion as to what establishes a mental health disorder such as depression. In the research

conducted by van den Bree et.al. (2009), 38 individuals classified as homeless but living in a

group home or shelter were not included in the study, and all participants were provided

information ahead of time without knowledge of the hypothesis (blind study), with a belief of

THESIS DRAFT 15

reduced predisposition in their reporting. Self –reporting was a concern in past trauma effecting

homelessness and mental health in the study by Shelton (2006). In the studies conducted by

Rosario Et Al (2012) recruiting of individuals from gay-focused programs along with a smaller

sample size could show some limitations as well by potentially narrowing the true amount of

individuals associated with homelessness and mental health.

Recommendations

Centered on the results of the meta-analysis, I believe in moving forward the correlation

between homelessness and depression should be dispersed into separate studies so that we can

independently examine which is more likely to cause the other one to happen. Thus I would

conduct a meta-analysis of studies where individuals originally classified with mental illness are

gathered up and counted as a sample size to see how many of “X” individuals were initially

homeless and wound up with mental illness. In a second meta-analysis I would gather

individuals classified just mentally ill and then see how many wind up homeless. Additionally I

believe study design should be examined to see if any flaws existed in reporting as there was a

rather large NIa and NIc conglomeration in some of the studies, which I feel overshadow the

other studies in the meta-analysis that have fewer. I will say however, by having a wide range

of results you get a very broad range of outcomes which in turn makes me want to dissect the

study down further to see what is the main reason certain studies had a higher follow up rate than

their peers. A major issue prevalent in the studies were identifying if the antecedent was

depression causing homelessness, or homelessness causing depression. If all studies would have

been able to pinpoint the exact causation of the two cohorts it would have been a more valuable

study in terms of finding what caused the other. With there being no clear cut definition of what

constitutes mental health illnesses, I believe the range of respondents was far too broad to get a

THESIS DRAFT 16

good grasp on the data. If someone was bipolar for instance they may have potentially had a

higher likelihood of not being homeless as compared to someone who might suffer from the

calamities of being diagnosed with schizophrenia. To make this study more concise, taking an

exact form of mental health illness and examining a cluster of individuals in comparison who are

either currently taking medicine, or not taking medicine, would be valuable in figuring out not

only if a set type of mental illness is a key in homelessness causation, but if medication can help

exacerbate or cure some of the woes associated with the link in homelessness and depression.

Implications for Social Change:

Social change needs to occur among younger individuals to help circumvent mental

health issues and homelessness later on. According to van den Bree ET. Al (2009) “Among a

range of well-established risk factors, a troubled family background, school adjustment problems

and experiences of victimization were found to be the strongest predictors of homelessness in a

general population of young people.” Additionally in the study by Shelton et Al. (2009)

adversity of childhood came in to factor of becoming homeless or having mental health illness

with instances such as having an addiction problem or becoming affiliated with a gang.

Conclusion

No matter which causes the other, the end result is never one that equates to a positive

ending. We need to be able to find ways to combat homelessness or mental illness from the

beginning so it won’t carry on down the line and create worse problems eventually. Sadly a lot

of the issues stem from lack of education when exploring why we have rampant depression or

homelessness, and in many cases both (van den Bree, 2009). Education needs to be available to

THESIS DRAFT 17

help those individuals struggling succeed in life. The homelessness aspect I believe it was said

best by Munia Khan (n.d.); “Aren't we all homeless without a home inside our mind?

References

Choi, N. G., & Morrow-Howell, N. (2007, July). Low-income older adults’ acceptance of

depression treatments: Examination of within-group differences. Aging & Mental

Health, 11, 423–433. http://dx.doi.org/10.1080/13607860600963802

Denler, H., Wolters, C., & Benzon, M. (2014, January 28). Social Cognitive Theory.

Retrieved from http://www.education.com/reference/article/social-cognitive-

theory/

Dividend Mantra. (2014, April 24). What’s The Bigger Sacrifice: Living Below Your

Means Or Working For Most Of Your Life? Retrieved from

http://www.dividendmantra.com/2014/04/whats-the-bigger-sacrifice-living-below-

your-means-or-working-for-most-of-your-life/

Dombeck, M., & Wells-Moran, J. (2006, July 3). Self Identity Problems-Online Self-Help

Book for Mental Health, Mental Illness. Retrieved from

http://www.mentalhelp.net/poc/view_doc.php?type=doc&id=9697

Iyengar, S. (1990, March). Framing Responsibility for Political Issues: The Case of

Poverty. Political Behavior, 12, 19-23. http://dx.doi.org/doi: 10.1007/BF00992330

Khan, M. (2012). Beyond the vernal mind. S.l.: Xlibris.

Kontopantelis, E., & Reeves, D. (2009). MetaEasy [Computer add-in for Excel]. :

Microsoft.

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Martin, G. (2013, October 28). Research Methods - Introduction [Video file] [Video file].

Retrieved from https://www.youtube.com/watch?v=PDjS20kic54

MetaAnalysis Table 1 MetaAnalysis Statistics of Relationship of Depression and

Homelessness-AaronSweazy [Analytical Table].

MetaAnalysis Table 2. MetaAnalysis Statistics of Relationship of Depression and

Homelessness-AaronSweazy [Analytical Table].

MetaAnalysis Table 3. MetaAnalysis Statistics of Relationship of Depression and

Homelessness-AaronSweazy [Analytical Table].

Microsoft (2014). Microsoft Excel [MetaEasy Spreadsheet]. : .

Moser, C. O. (1996). Confronting crisis: A comparative study of household responses to

poverty and vulnerability in four poor urban communities

Muhlhausen, D. B., & Tyrell, P. (2014). The 2013 Index of Dependence of

Government. . Retrieved from

http://www.heritage.org/resarch/reports/2013/11/the-2013-index-of-dependence-

on-government

National Institutes of Health (NIH), & National Institute of Neurological Disorders and

Stroke (NINDS) (2015, February 23). Dementia Information Page: Nationals

Institutes of Health (NIH) and National Institute of Neurological Disorders and

Stroke (NINDS). . Retrieved from

http://www.ninds.nih.gov/disorders/dementias/dementia.htm

Reardon, M. L., Burns, A. B., Preist, R., Sachs-Ericsson, N., & Lang, A. R. (2003).

Alcohol Use and Other Psychiatric Disorders in the Formerly Homeless and

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Never Homeless:Prevalence, Age of Onset, Comorbidity, Temporal Sequencing,

and Service Utilization. Substance Use & Misuse. doi:10.1081/JA-120017387

Reid, K. (2006). Interpreting and understanding meta-analysis graphsA practical guide

(Master’s thesis, The University of Adelaide, South Australia. ). Retrieved from

https://thesis.library.adelaide.edu.au/dspace/bitstream/2440/43554/1/

hdl_43554.pdf

Rosario, M., Schrimshaw, E. W., & Hunter, J. (2012). Homelessness Among Lesbian,

Gay, and Bisexual Youth: Implications for Subsequent Internalizing and

Externalizing Symptoms. Journal of Youth and Adolescence.

doi:10.1007/s10964-011-9681-3

Santiago, C. D., Kaltman, S., & Miranda, J. (Eds.). (2013, February 1). Poverty and

mental health: how do low-income adults and children fare in psychotherapy?

[Special issue]. Journal of Clinical Psychology, 69(2).

http://dx.doi.org/10.1002/jclp.21951

Shelton, K. H., Taylor, P. J., Bonner, A., & Bree, M. V. (2009). Risk Factors for

Homelessness: Evidence From a Population-Based Study. Psychiatric Services.

doi:10.1176/appi.ps.60.4.465

The Federal Register & Department of Health and Human Services (HHS). (2015).

Annual Update of the HHS Poverty Guidelines (Document Number: 2015-01120).

Retrieved from

https://www.federalregister.gov/articles/2015/01/22/2015-01120/annual-update-

of-the-hhs-poverty-guidelines

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Unger, D. G., & Wandersman, A. (1985, April). The importance of neighbors: The social,

cognitive, and affective components of neighboring. American Journal of

Community Psychology, 13, 139-169. http://dx.doi.org/10.1007/BF00905726

United States Census Bureau. (2014). How the Census Bureau Measures Poverty.

Retrieved from

http://www.census.gov/hhes/www/poverty/about/overview/measure.html

University of Manchester. (n.d.). MetaEasy v1.0.5. Retrieved from

https://engage.cune.edu/learn/pluginfile.php/34147/mod_page/content/6/W2_meta-

help.pdf

Van den Bree, M., Shelton, K., Bonner, A., Moss, S., Thomas, H., & Taylor, P. J. (2009).

A Longitudinal Population-Based Study of Factors in Adolescence Predicting

Weissberg, R., & Buker, S. (1990). Writing up research: Experimental research report

writing for students of English. Retrieved from

https://xa.yimg.com/kq/groups/86967712/75348466/name/wur.pdf

World Health Organization. (n.d.). Mental health, poverty and development. Retrieved

from http://www.who.int/mental_health/policy/development/en/