Nurse leaders’ strategies to foster nurse resilience
Bibliographic record
Abstract
AIM: To identify nurse leaders' strategies to cultivate nurse resilience. BACKGROUND: High nursing turnover rates and nursing shortages are prominent phenomena in health care. Finding ways to promote nurse resilience and reduce nurse burnout is imperative for nursing leaders. METHODS: This is a qualitative descriptive study that occurred from November 2017 to June 2018. This study explored strategies to foster nurse resilience from nurse leaders who in this study were defined as charge nurses, nurse managers and nurse executives of a tertiary hospital in the United States. A purposive sampling method was used to have recruited 20 nurse leaders. RESULTS: Seven strategies are identified to cultivate nurse resilience: facilitating social connections, promoting positivity, capitalizing on nurses' strengths, nurturing nurses' growth, encouraging nurses' self-care, fostering mindfulness practice and conveying altruism. CONCLUSIONS: Fostering nurse resilience is an ongoing effort. Nurse leaders are instrumental in building a resilient nursing workforce. The strategies identified to foster nurse resilience will not only impact the nursing staff but also improve patient outcomes. IMPLICATIONS FOR NURSING MANAGEMENT: The strategies presented are simple and can be easily implemented in any settings. Nurse leaders have an obligation to model and enable evidence-based strategies to promote nurses' resilience.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".