Psychiatric Hospital Workers’ Exposure to Disturbing Patient Behavior and Its Relation to Post-Traumatic Stress Disorder Symptoms
Bibliographic record
Abstract
Background About 10% of health-care workers experience post-traumatic stress disorder (PTSD); the rate is higher among workers exposed to aggression. Objective We extended this research by examining PTSD and exposure to violence and other disturbing patient behaviors, among nursing and other staff on inpatient psychiatric units (forensic and nonforensic). Method Surveys were completed online or in person by 219 respondents (30% response rate). Participants indicated which disturbing behaviors they had been exposed to and ranked the worst three behaviors in each of three categories: most unpleasant to work with, most disruptive to patient care, and most upsetting. Most ( n = 192) also completed the PTSD Checklist (PCL). Results All but two participants reported exposure to at least one disturbing behavior and ranked violence, feces smearing, and screaming constantly as the worst experiences overall. On the PCL, 24% scored above the cut off for probable PTSD. Nursing staff had the highest scores, with no difference between nursing staff on forensic versus nonforensic units. PCL score showed a small positive correlation with the number of disturbing behaviors experienced. Conclusion PTSD symptoms are common among psychiatric hospital workers, not only nursing staff. Future research using clinical assessment, longitudinal designs, and measurement of nonviolent disturbing behaviors is recommended.
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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 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".