Enhancing Trauma Resilience in Nurses and Personal Support Workers: A Feasibility Study of an 8-Week Supportive Trauma Exposure Preparation Intervention
Bibliographic record
Abstract
Background Nurses and personal support workers (PSWs) frequently face trauma in their work without sufficient resources to manage the resulting emotional stress. This contributes to high rates of burnout, which have remained elevated since the COVID-19 pandemic. Purpose To address the need for effective interventions that mitigate the impact of trauma exposure in the healthcare workplace, we developed the Supportive Trauma Exposure Preparation (STEP) program, an 8-week virtual psychotherapy intervention. The aim of this study was to evaluate the feasibility, acceptability, and preliminary effectiveness of the STEP program in reducing burnout and enhancing resilience. Methods A pilot study was conducted with 35 nurses and PSWs in Ontario assessing the feasibility, acceptability, and preliminary effectiveness of the STEP program at three months follow-up. Participants attended 8 weekly psychotherapy sessions, provided feedback on their experiences, and completed measures of burnout, resilience, mood, anxiety, and work and life functioning. Results The study demonstrated strong feasibility and acceptability, with high participant engagement and satisfaction with the STEP program. Improvements in burnout and work functioning were observed at the three-month follow-up. Conclusions The STEP program shows promise as a novel intervention addressing the critical unmet need for preventing and managing the detrimental effects of trauma exposure among nurses and PSWs in the healthcare workplace. ClinicalTrials.gov Registration # NCT04682561 (URL: https://clinicaltrials.gov/study/NCT04682561 )
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 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".