Job strain in public transport drivers: Data to assess the relationship between demand-control model indicators, traffic accidents and sanctions
DOI: 10.1016/j.dib.2018.05.036
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Summary
This paper presents a dataset examining the relationship between job strain and traffic safety outcomes among professional public transport drivers. The study is motivated by the need to understand how psychosocial working conditions, specifically those defined by the Job Demand-Control (JDC) model, influence accident rates and traffic sanctions in high-stress occupational groups. The research focuses on 780 male professional drivers in Bogotá, Colombia, comprising city bus drivers, taxi drivers, and inter-urban bus drivers. The data was collected through a cross-sectional study using a structured, self-administered questionnaire. The primary instrument was the Job Content Questionnaire (JCQ), a validated tool for assessing psychosocial factors at work. The JCQ measured six scales: supervisor support, peer support, skill discretion, decision authority, psychological demands, and job insecurity. From these, "control at work" (decision latitude) and "job strain" (the ratio of psychological demands to decision latitude) were calculated. Participants also reported demographic data, including age, driving experience, and weekly driving hours, as well as traffic accidents and fines incurred over the preceding two years. Statistical analyses included descriptive statistics, bivariate correlations, and an analysis of variance (ANOVA) with Post-Hoc comparisons to evaluate differences between drivers classified into four JDC quadrants: high strain, low strain, active job, and passive job. The findings reveal significant associations between work stress indicators and safety outcomes. Descriptive statistics showed that drivers averaged 41.13 years of age, 17.6 years of driving experience, and 72.58 driving hours per week. Correlation analysis indicated that psychological demands and job insecurity were positively correlated with traffic accidents and fines, while control at work showed negative correlations with these safety outcomes. The quadrant-based analysis demonstrated that drivers in the "high strain" quadrant reported significantly higher mean rates of traffic accidents and fines compared to those in the "low strain" and "active job" quadrants. Specifically, Post-Hoc tests confirmed that high-strain drivers had significantly more accidents than low-strain and active-job drivers, and significantly more fines than low-strain drivers. The significance of this work lies in providing empirical evidence linking the JDC model of occupational stress to road safety performance. The dataset allows for further analysis of how specific psychosocial factors, such as low decision latitude and high psychological demands, contribute to risky driving behaviors and negative safety outcomes. These findings support the integration of occupational health perspectives into road safety strategies, suggesting that improving working conditions and reducing job strain could mitigate traffic accidents and sanctions among professional drivers. The availability of the full database facilitates comparative research across different occupational groups and service types.
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-18 |
| archive | success | openalex | — | — | 5 | 2026-06-25 |
| extract | success | pdftotext | — | — | 2 | 2026-06-26 |
| clean | success | clean | — | — | 1 | 2026-06-26 |
| chunk | success | chunk | — | — | 1 | 2026-06-26 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-26 |
| enrich | success | openalex | — | — | 1 | 2026-06-20 |
| promote | success | — | — | — | 1 | 2026-06-18 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-26 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-26 |
| verify | success | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.
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