The utility of a safety climate scale among workers with a work-related permanent impairment who have returned to work
Bibliographic record
Abstract
BACKGROUND: Safety climate (SC) is a robust leading indicator of occupational safety outcomes. There is, however, limited research on SC among workers who have returned to work with a work-related permanent impairment. OBJECTIVE: This study examined three propositions: (1) a two-level model of SC (group-level and organization-level SC) will provide the best fit to the data; (2) antecedent factors such as safety training, job demands, supervisor support, coworker support, and decision latitude will predict SC; and (3) previously reported associations between SC and outcomes such as reinjury, work-family conflict, job performance, and job security will be observed. METHOD: A representative cross-sectional survey gathered information about experiences during the first year of work reintegration. About one year after claim closure, 599 interviews with workers were conducted (53.8% response rate). Confirmatory factor analyses were conducted to test the factor structure of the SC construct. Further, researchers used correlation analyses to examine the criterion-related validity. RESULTS: Consistent with general worker populations, our findings suggest the following: (1) the two-factor structure of SC outperformed the single-factor structure in our population of workers with a permanent impairment; (2) correlations demonstrate that workplace safety training, decision latitude, supervisor support, coworker support, and job demands could predict SC; and (3) SC may positively impact reinjury risk, work-family conflict, and may increase job performance and job security. CONCLUSIONS: Our study validated a two-factor SC scale among workers with a history of disabling workplace injury or permanent impairment who have returned to work. Practical applications of this scale will equip organizations with the necessary data to improve working conditions for this population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".