Risk factors for stress among police officers: A systematic literature review
Bibliographic record
Abstract
BACKGROUND: Stress is common among police personnel leading to several negative consequences. OBJECTIVE: We performed a systematic literature review to identify risk factors for stress among police officers. METHODS: We searched PubMed and Scopus electronic databases through to July 2018 and we conducted this review according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. The Newcastle-Ottawa scale was used for studies quality assessment. RESULTS: After selection, 29 cross-sectional studies met the inclusion criteria and included in the review. The average quality of studies was low since no study was rated as having low risk of bias, three studies (10.3%) as moderate risk and 26 studies (89.7%) were rated as having high risk of bias. Stress risk factors were summarized in the following categories: demographic characteristics; job characteristics; lifestyle factors; negative coping strategies and negative personality traits. CONCLUSIONS: Identification of stress risk factors is the first step to create and adopt the appropriate interventions to decrease stress among police personnel. The early identification of police officers at higher risk and the appropriate screening for mental health disorders is crucial to prevent disease and promote quality of life.
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".