Health, Safety, and Wellness Concerns Among Law Enforcement Officers: An Inductive Approach
Bibliographic record
Abstract
Background : Although studies have assessed the impact of occupational risk factors on the health of law enforcement officers (LEO’s), few have involved (LEO’s) as informants in ways that allow their points of view to be heard directly. Thus, the objective of this study is to explore the occupational health, safety, and wellness (OHSW) concerns of (LEO’s). Methods : (LEO’s) working in Quebec, Canada were invited to answer an open-ended question regarding their OHSW concerns. Using a multi-stage content analysis, the collected answers were analyzed and coded by two members of the research team to identify the most recurrent concerns of (LEO’s). Findings : Five themes relating to the OHSW concerns of (LEO’s) were identified, namely, the work schedule , occupational stress , work equipment , workplace health promotion , and operational risks . Furthermore, our analyses highlighted differences in the concerns of (LEO’s) based on their level of experience and sex. Conclusions/Application to Practice : This study addresses a gap in the literature on the OHSW concerns from the perspective of (LEO’s). Overall, our results support that the work schedule and occupational stress associated with law enforcement are the two most recurrent concerns of (LEO’s). Thus, the results of this study further stress the need for police organizations to implement strategies and policies, which could mitigate the deleterious effects of these hazards on the overall wellness of (LEO’s).
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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 | 0.021 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".