1 Management Practices Affect Occupational Safety
Bibliographic record
Abstract
The vast majority of workers in developed countries take for granted that going to work daily does not compromise their physical safety. The data, however, may tell a different story. While there has been a decline in the annual number of occupational fatalities in the United States, there are still more than 6,000 fatal work injuries per year, with approximately 3.6 million disabling injuries. The costs in human suffering alone should be sufficient to challenge researchers, but there are other severe economic and social costs as well. The number of days of work lost because of occupational injuries in Canada between 1993 and 1996 exceeded the number of workdays lost due to labor unrest. Estimates from the European Union suggest that an average of 30 days of work is lost for each workplace accident. Moreover, it is estimated that the total cost of each workplace injury in Ontario, Canada, is $6,000 (CDN), with the cost of each workplace fatality being $492,000. The most frequent attempts to account for occupational safety have emphasized the so-called “accident prone” individual, ergonomic design of equipment, and/or external regulatory systems (i.e., legislation and collective bargaining) (see Sheehy & Chapman, 1987). The modal response by organizational researchers has been one of neglect. Less than 1% or organizational research has focused on occupational safety. This provides a unique challenge to occupational health psychology, and the present research program forms part of an endeavor to confront this problem. We argue that management action directly affect perceived safety climate. In this research program, we focus on the extent to which management actions and human resource management practices affect occupational safety.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".