CRICOS No. 00213J

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When Non-Significance Maybe Significant: Lessons Learned from a Study into the Development, Implementation and Evaluation of a Risk Assessment Tool for Fleet Settings. Authors: Wishart, D., Freeman, J., Davey, J., Wilson, A., Rowland, B. Presented by Dr. James Freeman - PowerPoint PPT Presentation

Text of CRICOS No. 00213J

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CRICOS No. 00213JWhen Non-Significance Maybe Significant: Lessons Learned from a Study into the Development, Implementation and Evaluation of a Risk Assessment Tool for Fleet SettingsAuthors: Wishart, D., Freeman, J., Davey, J., Wilson, A., Rowland, B.Presented by Dr. James FreemanInternational Conference of Driver Behaviour and Training, Paris 29-30 November 2011Introduction (1)This study reports on the development of a self report assessment tool to increase the efficacy of crash prediction within Australian Fleet settingsOver last 20 years an array of measures have been produced (Driver anger scale, Driving Skill Inventory, Manchester Driver Behaviour Questionnaire, Driver Attitude Questionnaire, Driver Stress Inventory, Safety Climate Questionnaire)Introduction (2)While these tools are useful, research has demonstrated limited ability to accurately identify individuals most likely to be involved in a crash.Reasons cited include;Crashes are relatively rareOther competing factors may influence crash eventOngoing questions regarding the validity of self report measures (common method variance etc)Lack of contemporary issues relating to fleet driving performanceAimsIdentify contemporary driving issues for professional drivers (via focus groups) which has previously been published (refer Wishart, Davey, Freeman, and Rowland, 2009);Develop a self report risk assessment tool that is designed to increase the efficacy of crash prediction within Australian fleet settings; and Pilot and evaluate the assessment measure on a sample of professional drivers to determine its efficacy in predicting self report crashes.

MethodologyPhase onefocus groups to identify issues influencing driving behaviour in Australian work settings (217 participants, 160 males, 57 females)Phase two Questionnaire Development Issues identified in phase one Previous research in this current program of researchAdministration of Questionnaire (546 participants, 246 males 300 females)Questionnaire was administered as part of road safety training sessions.Employees of government organisationsIncorporated both urban and rural regions of Qld

Sample demographics71% tool of tradePassenger vehicles 65%44% of participants spent largest proportion of their driving on a combination of city and rural roads

60% reported driving between 1-10 hours per week (quite low)CrashesPreceding 2 year period 10% reported one crash while driving for work and 1.3% two crashes 1.1% three or morePreceding 2 year period 11% reported one crash while driving outside of work, 1.3% two crashes, no participants 3 or moreMinor damage incidents 16% one incident, 2.6% two incidents, 1.1% three or more

InfringementsPreceding 2 year period 8.5% one infringement while driving for work, 0.4% two infringements, 0.2% three or more Preceding 2 year period outside of work 13.2% one infringement, 3.5% two infringements, 1.1% three or more5Results (Factor analysis) Factor analysis identified 9 factors (speeding & aggression traditional DBQ)

Factor analysis resulted in 9 factorsTwo of these were traditional DBQ related items (speed and aggression)

6Results (Frequency of behaviours) Three highest ranked negative behaviour items I regularly overtake slow drivers rather than sit behind them (M=3.72, SD= 1.43) I regularly drive a few kms an hour over the speed limit on the highway (M=3.39, SD= 1.52) I regularly find myself driving on autopilot on the way home from work (M= 3.01, SD= 1.44)

Three highest ranked positive items

I pullover before answering/making mobile phone calls (M= 4.75, SD= 2.06)I drive to the speed limit no matter how much I am running late (M= 4.47, SD= 1.82)I regularly check my fluid levels and tyre pressure between scheduled servicing (M= 3.71, SD= 1.95) Results (Inter-correlations)Contrast to previous researchNo significant difference drivers age and kilometres travelledNo significant drivers age and frequency self reported crashesConsistent with previous researchAge significant negative relationship to speeding, aggression, time pressure, distraction, casualness, fatigueSignificant positive relationship kilometres travelled and all factors except awareness

Results (prediction of crashes & offences)Series of logistic regression analyses performed to determine identified factors predictive of self report crashes and offences work related and non work related. Overall model significant in some cases but.... No more than 8% of crashes, 3% of offences, 10% minor damage incidents

DiscussionNo factors were predictive of crash involvement either for work or personal driving. Kilometres travelled did not improve prediction of crashes.Concerns remain as to the reliability of self report measures & self report biasAlthough there may be benefits to utilising crash databases these can also contain biases (what is defined as a crash)Concerns over potential publication biases primarily publishing statistically significant results

Mark your Diaries!International Occupational Safety in Transport ConferenceSeptember 2012, Gold Coast.

Table 3. Means, standard deviations and alpha reliability coefficients for the behaviour questionnaire factors

MSD

F1 - Speeding (8 items)2.881.01.88

F2 - Aggression (7 items)2.16.78.85

F3 - Time pressure (5 items)1.95.76.78

F4 - Distraction (5 items)2.221.04.72

F5 - Casualness (4 items)2.12.85.61

F6 - Awareness (2 items)2.45.96.39

F7 - Maintenance (2 items)3.191.64.74

F8 - Fatigue (4 items)2.661.05.61

F9 - Minor damage (2 items)1.59.70.43

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