Systematic review of posttraumatic stress disorder in police officers following routine work‐related critical incident exposure
Bibliographic record
Abstract
BACKGROUND: The prevalence of PTSD in police officers has been the subject of a large and highly variable empirical literature. The present systematic review evaluates the extant literature on PTSD in police officers using an international dataset. METHODS: We employed best-evidence narrative synthesis to evaluate whether PTSD prevalence in police is elevated in comparison to the general population of Canada (8%), which itself has a higher lifetime PTSD prevalence than many other regions and thus serves as a conservative standard of comparison. RESULTS: PTSD prevalence in police varied considerably across studies from 0% - 44% (M = 14.87%, Median = 9.2%). Despite this variability, strong evidence exists to suggest PTSD prevalence is elevated in police officers. Examination of possible sources of variability in prevalence outcomes highlighted substantial variability in outcomes due to the selection of measurement tool for assessing PTSD (e.g., DSM vs. IES). Examination of commonly-assessed predictive factors for PTSD risk across the literature showed that individual-difference factors (e.g., age, years of service) bear weak-to-nonexistent relationships with PTSD risk, while incident-specific factors (e.g., severity of exposure) are more strongly and consistently associated with PTSD prevalence. Organizational factors (e.g., low support from supervisor) are at present understudied but important possible contributors to PTSD risk. CONCLUSIONS: PTSD prevalence is elevated in police officers and appears most strongly related to workplace exposure. Measurement variability remains a critical source of inconsistencies across the literature with drastic implications for accurate detection of officers in need of mental health intervention.
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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 | 0.010 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.014 | 0.014 |
| 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) | 0.006 | 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".