There Is Hope in Safety Promotion! How Can Resources and Demands Impact Workers’ Safety Participation?
Bibliographic record
Abstract
Promoting workplace safety is crucial in occupational health and safety (OHS). However, existing studies have primarily concentrated on accident prevention, overlooking the role of resources in encouraging safety. This research investigates the impact of a personal resource, namely hope, on safety participation, considering its interaction with job resources and job demands using the Job Demands-Resources (JD-R) model in the context of safety. A cross-sectional study was conducted in a large company managing European shopping centers (N = 262). Of the sample, 52.3% of participants were female. Data were collected through an online questionnaire and analyzed using model 92 of Andrew F. Hayes’ Process Macro to test the hypothesized moderate serial mediation model. Our results highlighted that (1) hope directly correlates with safety participation, (2) hope and job dedication mediate the relationship between autonomy and safety participation, and (3) high job demands can undermine the beneficial effects of resources (i.e., autonomy, hope, and job dedication) on safety participation. These results suggest that workers with personal resources like hope are more likely to engage in safety practices, displaying increased dedication and focus on safety. However, excessive job demands can challenge the effectiveness of these resources in promoting safety participation. This study offers a novel perspective by integrating safety participation into the JD-R model framework.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".