Simulation training to increase resilience of nursing groups (STRONG): Results from a multi-site trial
Bibliographic record
Abstract
Background Healthcare systems worldwide are undergoing increased staffing crises due in part to the impacts of the COVID-19 pandemic on the nursing workforce. Existing resilience-promotion programs for new graduate nurses were not designed to address the stressors seen in hospitals since 2020. Our aim was to create a training program that would prepare fourth year nursing students to successfully transition into hospital-based jobs. Methods A 10-week simulation-based online training program was developed; topics addressed included burnout and moral distress, mortality and trauma-informed care, self-advocacy, mental health, and resilience and coping strategies. Fourth-year students were recruited from across Canada and completed pre- and post-intervention questionnaires assessing resilience, self-efficacy, coping, and mental health components. Participants completed 10 training modules, including pre-simulation preparation materials, virtual simulation games, and self-debriefing questionnaires. Results In 94 participants, significant pre-to-post intervention increases were found for resilience and self-efficacy. Conclusion Our program was effective in improving several key metrics in fourth year nursing students and was well-received by participants. This program could be deployed as part of nursing program curricula or hospital onboarding.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".