The Interplay Between Supervisor Safety Support and Occupational Health and Safety Vulnerability on Work Injury
Bibliographic record
Abstract
BACKGROUND: Workers exposed to hazards without adequate protections are at greater risk of injury and illness. Supervisor activities have also been associated with injury risk. We examined the interplay between supervisor safety support and occupational health and safety (OHS) vulnerability on workplace injury and illness. METHODS: A survey was administered to 2,390 workers employed for more than 15 hrs/week in workplaces with at least five employees who had a direct supervisor. We examined the combined effects of hazard exposure with inadequate protection (OHS vulnerability) and supervisor support on workplace injury and illness, using additive interactions in log-binomial regression models. RESULTS: OHS vulnerability and lack of supervisor support independently increased the likelihood of physical injuries at work. Crude and adjusted models showed that the risk of physical injury was at least 3.5 times higher among those experiencing both OHS vulnerability and a lack of supervisor support than individuals without OHS vulnerability and with a supportive direct supervisor. Workers who experienced vulnerability were at less risk if they had a supervisor who was supportive. CONCLUSION: In workplaces where workers experience one or more types of OHS vulnerability, having a supportive supervisor may play an important role in reducing the risk of injury and protecting workers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".