Traumatic events in the workplace: impact on psychopathology and healthcare use of police officers.
Bibliographic record
Abstract
This retrospective study examined the impact of exposure to duty-related traumatic events and of Posttraumatic Stress Disorder (PTSD) among 159 Canadian police officers. Structured interviews were conducted (1) to assess the presence or absence of exposure to work-related traumatic events; (2) to identify the most traumatic incident; (3) to determine PTSD status (i.e., full, partial or no PTSD); and (4) to diagnose psychopathology (i.e., anxiety, depression, and substance-related disorders). Healthcare use, hardiness, and coping were assessed with self-administered questionnaires. Data were analyzed using chi-square tests, Fisher exact tests, and Student's t-tests. Results showed that trauma-exposed officers were no more likely to have psychopathology at time of study and did not score differently on measures of hardiness and coping than non-exposed officers. However trauma-exposed officers who developed full or partial PTSD were significantly more likely to experience depression in the aftermath of trauma than exposed officers without PTSD. After the trauma, police with full PTSD were significantly more likely to have medical appointments, consult a mental health professional, be on sick leave, and score lower on a hardiness measure than officers without PTSD. Full PTSD affected subsequent psychopathology, healthcare use, and hardiness. Clinical implications of the findings are discussed.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".