Strategies to Reduce the Impact of Trauma in Psychiatric Nurses: An Integrative Review of the Literature
Bibliographic record
Abstract
BACKGROUND: Psychiatric nurses are at a higher risk for exposure to violence and aggression, leading to potential burnout, moral injury, post-traumatic stress, and turnover. There is little evidence of a preferred strategy to support nurses and decrease the impact of traumatic experiences on psychiatric nurses. The aim of this integrative review was to explore potential strategies to decrease the impact of traumatic experiences among nurses in psychiatric settings. METHODS: Following a systematic search of PsycINFO/Ovid, CINAHL, and MEDLINE/Pubmed, Joanna Briggs Institute quality appraisal tools were used to analyze quality of the articles. Thirteen articles met the inclusion/exclusion criteria for this study. Data were analyzed and synthesized into three key themes and seven sub-themes. RESULTS: Three themes were noted to be common to the included texts. 1. Interpersonal Supports (Formal Support, Peer/Supervisor Support, and Informal Family/Social Support). 2. Organizational Supports (Perception of Job Safety/Satisfaction, Promoting Personal Resilience, Supporting Team Resilience, and Organizational Commitment to Resilience). 3. Protection of Personal Resources. CONCLUSIONS: Many common suggestions for decreasing the impact of exposure to violence and trauma were noted across the thirteen articles, however, there is little evidence of a preferred strategy, how strategies are developed and employed or the efficacy of any particular strategy. Further investigation is needed to identify and evaluate supportive interventions, their feasibility and efficacy.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".