Moderation in all things, except when it comes to workplace safety: Accidents are most likely to occur under moderately hazardous work conditions
Bibliographic record
Abstract
Abstract In this article, we argue that the relationship between workplace hazardousness and accidents is best characterized as an inverted‐U, such that accidents are most likely to occur within moderately hazardous environments. Specifically, whereas highly hazardous work environments are strong situations in which there is a clear need for a high degree of safety behavior, the amount of safety behavior needed to minimize accidents within moderately hazardous environments is more ambiguous. Drawing on self‐regulatory theories of work motivation, we argue that most individuals tend to exhibit a proportional response to hazardousness, such that moderately hazardous environments are met with a moderate degree of safety behavior. However, we demonstrate that proportional responses to hazardousness will ultimately yield an inverted‐U relationship between hazardousness and accidents. Instead, a sharp, non‐linear increase in safety behavior is needed to keep accidents at a low and constant level as hazardousness increases. We present four studies to test our hypotheses. Studies 1 and 2 used archival data to test our hypothesis of an inverted‐U relationship between hazardousness and accidents in natural work settings. Studies 3 and 4 were experiments which replicated this finding, and more importantly, demonstrated that the inverted‐U relationship between hazardousness and accidents was driven by a failure to sharply increase safety behavior in response to small increases in hazardousness. We conclude with a discussion of the implications of these results for the safety literature, particularly the need to educate workers regarding the pattern of safety behavior needed to fully offset environmental hazardousness.
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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 | 0.007 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".