Contribution of Critical Events and Chronic Stressors to PTSD Symptoms Among Psychiatric Workers
Bibliographic record
Abstract
OBJECTIVE: Psychiatric staff are exposed to critical events (e.g., violence, physical threats) in the workplace and thus are at risk of posttraumatic stress disorder (PTSD). The authors examined the prevalence of PTSD symptoms among psychiatric hospital staff in Canada and the role of potentially traumatic critical events and chronic stressors (e.g., witnessing patients engaging in self-injury) in affecting psychiatric staff's mental health. METHODS: The authors analyzed cross-sectional survey data from 761 psychiatric staff (69% female, 57% nursing, 64% with more than 5 years of experience in mental health). The analysis focused on questions about exposure to critical events and chronic stressors. RESULTS: Sixteen percent of participants met a screening cutoff score on the PTSD Checklist-5, a self-report PTSD measure. Almost all staff (96%) had been directly or indirectly exposed to at least one critical event, and two-thirds (67%) had been directly exposed to at least one such event. Nursing staff reported higher scores than did allied health staff. A regression analysis yielded a model in which both critical events and chronic stressors were significant contributors to the variance in PTSD symptoms; professional discipline and gender did not explain additional variance. CONCLUSIONS: PTSD is a significant concern for psychiatric staff. Exposure to violence and chronic stressors were found to contribute significantly and independently to explaining PTSD symptom checklist scores.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".