Workplace interventions to prevent or reduce post‐traumatic stress disorder and symptoms among hospital nurses: A scoping review
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to identify literature on evaluated workplace interventions to prevent or reduce the prevalence or impact of work-related post-traumatic stress disorder (PTSD) and PTSD symptoms among hospital nurses. A second objective was to summarise and compare the characteristics and effectiveness of these interventions. BACKGROUND: A substantial proportion of nurses report PTSD symptoms. Previous reviews have synthesised interventions to address PTSD in military and other high-risk populations, but similar work focusing on nurses has yet to be conducted. METHODS: We conducted a scoping review with the question: What interventions have been studied to prevent or treat PTSD symptoms or PTSD among nurses working in hospitals? We followed the PRISMA Scoping Review Checklist using an unregistered protocol. We searched in twelve academic and grey literature databases (e.g. MedLine, CINAHL) with no language restrictions. We included publications reporting on interventions which were evaluated for measurable impacts on PTSD and PTSD symptoms among nursing staff working in inpatient settings from 1980 to 2019, and charted study characteristics in a spreadsheet. RESULTS: From 7746 results, 63 studies moved to full-text screening, and six studies met inclusion criteria. Methodologies included three randomised controlled studies, one quasi-experimental study, one pre-post feasibility study and one descriptive correlational study. Four studies reported a significant reduction in PTSD scores in intervention groups compared with baseline or comparison, when using debriefing, guided imagery or mindfulness-based exercises. CONCLUSIONS: This review identified six studies evaluating hospital-based interventions to reduce PTSD and PTSD symptoms among hospital nurses, with some positive effects reported, contributing to a preliminary evidence base on reducing workplace trauma. Larger studies can compare nurse subpopulations, and system-level interventions should expand the focus from individuals to organisations. RELEVANCE TO CLINICAL PRACTICE: This review can inform nursing and hospital leaders developing evidence-based interventions for PTSD among nurses.
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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 | 0.018 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".