Post-traumatic stress disorder in inpatient psychiatry staff: a narrative review
Bibliographic record
Abstract
Introduction: Healthcare workers have high rates of post-traumatic stress disorder (PTSD). The aim of this study is to provide a narrative review of the literature on PTSD in psychiatrists, psychiatric nurses, and other healthcare staff working on psychiatric inpatient units. Methods: A search was conducted on MEDLINE for studies in English. Studies were included if they studied a population of inpatient psychiatric staff and had PTSD as an outcome measure. Of the 1487 articles the search returned, 26 were included. Results: Rates of post-traumatic stress ranged from 0% to 24%, depending on the measurement tool. Nurses had higher rates of PTSD compared to other psychiatric unit staff. There was no difference in PTSD prevalence between forensic and non-forensic psychiatric units. There were mixed findings on the association between gender and age and PTSD. Exposure to suicide, verbal aggression, physical violence, and other disturbing patient behaviors were associated with a higher risk of PTSD. Conclusion: High rates of post-traumatic stress are seen in healthcare workers in inpatient psychiatric settings. More research is needed on interventions to reduce PTSD in this population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".