Resilience development in nursing students and new nurse graduates: a qualitative umbrella review protocol
Bibliographic record
Abstract
OBJECTIVE: The objective of this umbrella review will be to synthesize qualitative evidence on the experiences of resilience and resilience development in nursing students and new nurse graduates. INTRODUCTION: The COVID-19 pandemic drew attention to longstanding issues of burnout and stress among nurses. While the crisis has abated, burnout remains higher than in pre-pandemic levels. Within the nursing context, it has been shown that resilience enables nurses to adapt to workplace stressors positively. ELIGIBILITY CRITERIA: This review will include qualitative systematic reviews and meta-syntheses on resilience and resilience development in nursing students and new nurse graduates. There will be no limitations on participant age, type of academic program, gender, or ethnicity. METHODS: This review will follow the JBI methodology for umbrella reviews. A systematic search will be conducted of MEDLINE (Ovid), CINAHL (EBSCOhost), and Embase (Ovid) to locate qualitative systematic reviews and meta-syntheses. Gray literature will be searched using Google Scholar and ProQuest Dissertations and Theses Global (ProQuest). The reference lists of all included reviews will also be searched for relevant papers. Two reviewers will independently screen titles and abstracts, and then full texts, against the eligibility criteria. The JBI Critical Appraisal Instrument for Systematic Reviews and Research Syntheses will be used to assess methodological quality of the data. Data will be extracted using a modified version of the JBI data extraction tool for systematic reviews and research syntheses. A narrative summary and tables will be used to present the review characteristics and findings. Key synthesized findings will be displayed in a Summary of Evidence, with conclusions and relevant recommendations for practice and research provided. REVIEW REGISTRATION: PROSPERO CRD420250655717.
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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 | 0.147 | 0.106 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.012 | 0.010 |
| Bibliometrics | 0.019 | 0.013 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.070 | 0.013 |
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".