When Nurses Leave: A Critical Incident Study of Turnover Intentions
Bibliographic record
Abstract
AIM: The aim of the study was to identify critical incidents that may have led nurses to consider leaving their practice setting or their profession. DESIGN: A qualitative design using the Critical Incident Technique (CIT) within a larger mixed-methods study. METHODS: Participants were asked to write about a problematic situation in their workplace which made them consider leaving their job or the profession. Thematic analysis was used to analyse the data. The COREQ checklist for qualitative research was used in this study. RESULTS: There was a total of 350 critical incidents written by 187 nurses. Four themes were identified: (1) working within constraints (2) abandoned (3) compromised care and (4) unpredictability of work. An underlying issue throughout the nurses' stories was the perception of not being respected or valued. CONCLUSION: Making the decision to leave was not based on any one single event, but rather on a process that evolved over time based on many challenging situations that nurses encountered. IMPLICATIONS FOR THE PROFESSION: Understanding the complex reasons that lead nurses to consider leaving is essential in order to identify targeted interventions to retain nurses. IMPACT: This study addresses the issue of nurses' intentions to leave. One of the findings suggests that nurses need to feel valued and want to be more involved in unit-based decisions. The results of this study will help managers gain a better understanding of why nurses consider leaving. REPORTING METHOD: The Consolidated Criteria for Reporting Qualitative Research (COREQ) guidelines for qualitative research were followed. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution.
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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 | 0.023 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".