Stress-related challenges regarding the psychological health of police officers: The roles of perceived stress and physiological stress
Bibliographic record
Abstract
The police profession is recognized as fraught with high levels of stress. This stress arises from exposure to various stressors, including demanding work schedules, overtime, administrative responsibilities, peer relationships, and exposure to potentially traumatic events. This stress has the potential to affect psychological health, which can be measured using two components: well-being and distress. This study assessed whether physiological and psychological stress contribute to the psychological health of police officers in Quebec (n = 25). Electronic questionnaires were distributed to assess perceived stress [Depression, Anxiety, and Stress Scale (DASS-21)] and Psychological Health at Work (Gilbert et al., Citation2011). Hair samples were collected to measure cumulative cortisol over a three-month period. The findings suggest a positive correlation between perceived stress and physiological stress (r = .416, p = .039). Moreover, the combination of both stress measurements (physiological and perceived stress) provided the best explanation of police officers’ psychological health, F(2, 22) = 8.243, p = .002, R2 = .428, p = .022 and their psychological distress F(2, 22) = 5.832, p = .009, R2 = .346, p = .037. This suggests that both components of police officers’ psychological health can be predicted using various stress metrics, including self-report and physiological measures.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".