Analysis of nurses’ intention to resign and its reasons in a tertiary Grade-A hospital in Beijing during the post-pandemic era
Bibliographic record
Abstract
Background and objective: After the end of the COVID-19 pandemic in China in 2023, there has been an increasing number of clinical nurses resigning, which has greatly impacted clinical work. The objective of this study was to understand the reasons for the resignation of clinical nurses in a tertiary Grade-A hospital in Beijing.Methods: This study was completed in two stages. In the first stage, semi-structured interviews were conducted with 11 nurses who submitted resignation applications between August 2022 and August 2023 at a tertiary Grade-A hospital in Beijing. The themes identified were analyzed and refined using NVivo 12.0 software. The second stage involved a convenience sampling method for conducting a questionnaire survey on the resignation intentions of 220 clinical nurses in the hospital from September to October 2023.Results: The results of the first stage research show that the reasons for nurses' resignation can be summarized into four themes: deteriorating team collaboration atmosphere, heavy workload, conflicting family roles, and significant fatigue. The results of the second stage of the research indicated that out of 220 clinical nurses surveyed, 12 nurses reported plans to resign in the upcoming year. And we found that “Do You Have Intention to Resign within One Year” Yes vs No were statistically different in terms of commuting time (p = .048), work intensity (p = .049), physical health status (p = .001), reasonableness of work input and income (p = .002), promotion opportunities (p = .046), reward and punishment system (p = .001), and humanistic care (p = .001), and hospital nursing management methods (p = .001).Conclusions: The poor rationality of work input and income, deteriorating team collaboration atmosphere, and heavy workload may be the main reasons for nurses resigning. Nursing managers need to enhance nurses' salaries and benefits, strengthen hospital humanistic care, create a harmonious team work atmosphere, and emphasize the professional development of nursing talent to ensure the stability of the nursing team, especially during times of epidemic outbreaks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".