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Record W4400988054 · doi:10.5430/jnep.v14n11p44

Analysis of nurses’ intention to resign and its reasons in a tertiary Grade-A hospital in Beijing during the post-pandemic era

2024· article· en· W4400988054 on OpenAlexvenueno aff
Jie Li, Juan Cheng, Yinping Zhou, Runxi Tian, Y Duan, Yan Liu

Bibliographic record

VenueJournal of Nursing Education and Practice · 2024
Typearticle
Languageen
FieldMedicine
TopicMedical Research and Treatments
Canadian institutionsnot available
FundersBeijing University of Chinese Medicine
KeywordsBeijingPandemicCoronavirus disease 2019 (COVID-19)Tertiary careMedicineTertiary levelSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)NursingPolitical sciencePsychologyFamily medicineChinaLawInternal medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.436
Threshold uncertainty score0.518

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.044
GPT teacher head0.454
Teacher spread0.410 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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