Best Practices to Promote Occupational Safety and Satisfaction: A Comparison of Three North American Hospitals
Bibliographic record
Abstract
PURPOSE: Hospitals in North America consistently have employee injury rates ranking among the highest of all industries. Organizations that mandate workplace safety training and emphasize safety compliance tend to have lower injury rates and better employee safety perceptions. However, it is unclear if the work environment in different national health care systems (United States vs. Canada) is associated with different employee safety perceptions or injury rates. This study examines occupational safety and workplace satisfaction in two different countries with employees working for the same organization. METHODOLOGY/APPROACH: Survey data were collected from environmental services employees (n = 148) at three matched hospitals (two in Canada and one in the United States). The relationships that were examined included: (1) safety leadership and safety training with individual/unit safety perceptions; (2) supervisor and coworker support with individual job satisfaction and turnover intention; and (3) unit turnover, labor usage, and injury rates. FINDINGS: Hierarchical regression analysis and ANO VA found safety leadership and safety training to be positively related to individual safety perceptions, and unit safety grade and effects were similar across all hospitals. Supervisor and coworker support were found to be related to individual and organizational outcomes and significant differences were found across the hospitals. Significant differences were found in injury rates, days missed, and turnover across the hospitals. ORIGINALITY/VALUE: This study offers support for occupational safety training as a viable mechanism to reduce employee injury rates and that a codified training program translates across national borders. Significant differences were found.between the hospitals with respect to employee and organizational outcomes (e.g., turnover). These findings suggest that work environment differences are reflective of the immediate work group and environment, and may reflect national health care system differences.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".