Evaluating the Effectiveness of Fatigue Management Training to Improve Police Sleep Health and Wellness
Bibliographic record
Abstract
OBJECTIVES: To evaluate the effectiveness of an intervention for improving sleep health in a sample of employees from the Royal Canadian Mounted Police (RCMP). METHODS: Using a pre- and post-design we exposed 61 RCMP members to a fatigue-management training program. Pre- and post-intervention surveys included the Pittsburg Sleep Quality Index (PSQI), the World Health Organization Quality of Life (WHOQOL) instrument, and the six item index of psychological distress (Symptom Checklist-90). RESULTS: We found the training improved member satisfaction with sleep (Wald = 2.58; df = 1; P = 0.03) and reduced symptoms of insomnia (Wald = 5.5; df = 1; P = 0.02). Furthermore, the training reduced the incidence of headaches (Wald = 6.5; df = 1; P = 0.01). CONCLUSIONS: Our findings suggest that a fatigue management training program resulted in positive sleep health benefits for police. We stress the importance of continued evaluation to inform the large-scale implementation of fatigue-management programs.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".