Accident risk at work: Using the O*NET database to construct a unidimensional factor of occupational risk for workplace accidents

Bauerle, Timothy; Magley, Vicki J.

doi:10.1037/e604062012-247

Cited by 2 publications

(14 citation statements)

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“…Early on, Zohar (1980) identified perceived level of risk at the workplace as one of the eight dimensions of safety climate. His seminal work on development and validation of a 40-item measure of safety climate forms the basis for many of the measures existing today (e.g., Bauerle & Magley, 2012). Brown and Holmes (1986) examined a shorter version of Zohar's (1980) scale in a US context and identified physical risk as one of the factors comprising safety climate.…”

Section: Occupational Risk Factormentioning

confidence: 99%

“…The inspiration for the present study stemmed from Bauerle and Magley's (2012) (Ford & Wiggins, 2012). In addition, jobs with greater physical ability requirements (e.g., construction jobs) are also more prone to accident occurrence.…”

Section: Research Questionsmentioning

confidence: 99%

“…Findings on critical predictors can then be generalized beyond any specific job or organization and highlight occupations that are vulnerable to injuries (Ford & Wiggins, 2012). By identifying occupational features that help avoid safety hazards, industries can design or redesign safe jobs and focus safety assessments depending on the category of risk (Bauerle & Magley, 2012). Further, development of an occupational risk index based on the critical predictors can be used to aid cross-occupational safety research and guide future research on other cross-occupational outcomes.…”

Section: Introductionmentioning

confidence: 99%

“…Ford and Wiggins (2012) found that cognitive ability and skill requirements, as measured by O*NET, were related to injury rates. Building on the literature surrounding organizational determinants, Bauerle and Magley (2012) built an occupational risk factor from targeted O*NET work context and work activity descriptors and showed that it was related to perceived risk and safety climate. While such findings indicate that O*NET data can be used to predict safety outcomes, the studies are limited in scale in that they do not take advantage of the full suite of worker and occupational data that is available.…”

Section: Introductionmentioning

confidence: 99%

“…Ford and Tetrick (2011) created an occupational hazard composite combining O*NET work context ratings with individual-level data and used it to predict employee attitudes and safety performance. Bauerle and Magley (2012) developed an occupation-level unidimensional construct of risk by drawing from O*NET work context ratings. The developed scale was also used by Bauerle (2012) to examine the relationship between occupational risk and perceived job risk within a broader framework studying the effects of masculinity on safety motivation and by Bauerle et al (2015) to understand men's risk for workplace fatalities.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Determinants of safety outcomes in organizations: Exploring O*NET data to predict occupational accident rates

Kumar

Burns

2022

Personnel Psychology

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Although workplace safety research is common given the frequent occurrence of fatal and nonfatal occupational accidents, it has focused mainly on safety climate and lacks a unified approach when examining predictors of safety outcomes. We argue that adopting an integrated approach with job analysis data and using newer machine learning methods can support and extend findings from cross‐sectional research studies using traditional statistical methods. The suggested approach is demonstrated by using three machine learning methods (elastic net, random forest, and gradient boosting) along with publicly available O*NET data to predict annual nonfatal occupational incident rates published by the US Bureau of Labor Statistics. Findings indicate that O*NET descriptors from several subdomains including abilities, work context, and work activities were significant in predicting occupational injury rates. The amount of variance explained by the predictors varied from 54.2% (gradient boosting) to 58.8% (elastic net) with 12 common predictors across the three methods. The exploratory approach with machine learning techniques supports past findings and helps uncover understudied predictors of safety outcomes. This study adds to the literature surrounding person‐ and situation‐based antecedents to workplace safety and has several other implications for research and practice.

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