Strengthening the Indomitable Spirit of Nurses Through Targeted Resilience Education
Bibliographic record
Abstract
Abstract Nurses face complex stressors in their work including routine exposure to human suffering and potentially traumatic events. Consequently, nurses are at risk of moral distress, workplace burnout, and compassion fatigue. The aim of this study was to design, develop, and test a health-promoting resilience education program for nurses. The research questions were as follows: (1) Are resilience scores of nurses affected by resilience education? (2) How do nurses understand resilience in the context of their workplace? (3) What role does resilience play in nurses’ mental health? (4) Is single-session targeted resilience education effective in maintaining resilience scores over time? Nurses in this study are moderately resilient as noted by their pre-education scores on the Resilience Scale (RS) and the Resilience at Work (RS@W) Scale. Resilience scores significantly increased immediately after resilience education and were sustained over time. Nurses have an array of health strategies for maintaining their resilience; these were further enhanced through experiential education. Increased resilience scores resulted in changes in nurses’ behavior and thinking, and new strategies were integrated into the nurses’ “toolbox” of cognitive and behavioral skills. Building and sustaining a strong foundation of resilience and well-being is key for nurses to maintain mental health, cope with work-related stressors, and provide safe competent patient care. Study outcomes offer opportunities to change the narrative from nursing as perilous and risky to one of strength, flourish, and growth. Beyond individual resilience, system-level change is required to support the well-being of healthcare personnel.
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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 | 0.002 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".