Coping among public safety personnel: A systematic review and meta–analysis
Bibliographic record
Abstract
Public safety personnel (PSP) are routinely exposed to potentially psychologically traumatic events (PPTEs) that, in turn, can result in posttraumatic stress injuries (PTSI), including burnout and increased symptoms of depression and anxiety. However, the longitudinal impact of PPTEs on PSP coping remains unclear. Coping can be operationalized as various strategies (i.e., behaviours, skills, thought and emotion regulation) for dealing with stressors, which are broadly categorized as either approach (adaptive, positive, social support) or avoidant coping strategies (maladaptive withdrawal, avoidance, substance use). This systematic review and meta-analysis aims to evaluate longitudinal coping outcomes among PSP. Thirteen eligible repeated-measures studies explicitly evaluated coping in 1854 police officers, firefighters, and rescue and recovery workers. Study designs included randomized-control trials, within-subject interventions and observational studies. Effect sizes (Cohen's d) at follow-up were described in 11 studies. Separate meta-analyses reveal small (d < 0.2) but non-significant improvements in approach and avoidant coping. Studies were of moderate quality and low risk of publication bias. Heterogeneity in outcome measures, follow-up durations, and study types precluded subgroup analyses. The current findings can inform the development and evaluation of organizational training programs that effectively promote sustained adaptive coping for PSP and mitigate PTSIs.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.020 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".