Workplace stressors and <scp>PTSD</scp> among psychiatric workers: The mediating role of burnout
Bibliographic record
Abstract
Critical workplace events (e.g., assaults), chronic stressors, burnout, and work conditions all affect nurse well-being. The present study investigated associations among these sources of stress and posttraumatic stress disorder (PTSD) symptoms in psychiatric nurses, hypothesizing that burnout would mediate the paths between workplace stressors and PTSD. Surveys were completed by 611 psychiatric nurses or allied health staff working on inpatient units in three psychiatric hospitals. Participants reported on critical events and chronic stressors specific to providing psychiatric care and completed the Maslach Burnout Inventory (MBI), Areas of Worklife Survey (AWS) (work conditions), and PTSD Checklist for DSM-5. Data were analysed using structural equation modelling. Burnout had a direct relation to PTSD symptoms and partially mediated the effect of exposure to critical events, but not chronic stressors, on PTSD symptoms. Chronic stressors related to patients' disturbing behaviour (e.g., flooding room, eating non-food items) had a direct effect on PTSD symptoms, but those related to resisting care (e.g., screaming constantly, physically resisting care) had no significant association. Worklife conditions had a negative direct effect on Burnout and indirect effect on PTSD, whereby participants reporting poorer alignment of work conditions with their expectations had higher Burnout and PTSD symptom scores. Different sources of workplace stress have different relations to PTSD symptoms, and Burnout has both direct and mediation effects. Interventions aimed at reducing patients' aggressive and disturbing acts and improving healthcare providers' burnout and worklife factors in hospitals may all be needed to reduce PTSD among psychiatric staff.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".