The relationships between organizational and individual variables to on-the-job driver accidents and accident-free kilometres
Bibliographic record
Abstract
Highway fatalities are the leading cause of fatal work injuries in the US, accounting for approximately 1 in 4 of the 5900 job-related deaths during 2001. The present study focused on the contribution of organizational factors and driver behaviours to on-the-job driving accidents in a large Western Canadian corporation. A structural equation modelling (SEM) approach was used which allows researchers to test a complex set of relationships within a global theoretical framework. A number of scales were used to assess organizational support, driver errors, and driver behaviours. The sample of professional drivers that participated allowed the recording of on-the-job accidents and accident-free kilometres from their personnel files. The pattern of relationships in the fitted model, after controlling for exposure and social desirability, provides insight into the role of organizational support, planning, environment adaptations, fatigue, speed, errors and moving citations to on-the-job accidents and accident-free kilometres. For example, organizational support affected the capacity to plan. Time to plan work-related driving was found to predict accidents, fatigue and adaptations to the environment. Other interesting model paths, SEM limitations, future research and recommendations are elaborated.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".