Factors influencing career plateau in nurses: a protocol for systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Career plateau is a situation that has many negative effects on individual nurses, nursing management and nursing profession. Despite global research examining career plateaus among nurses, a comprehensive data synthesis on its prevalence and influential factors is still missing. The study aims to systematically analyse the prevalence of career plateau in nurses and explore the related influencing factors. METHODS AND ANALYSIS: The anticipated start date for the study is December 2025, and the anticipated end date is August 2027. We will search PubMed, Web of Science, Embase, PsycINFO, CINAHL, Scopus, the China National Knowledge Infrastructure, the Wanfang database and the China Biomedical Literature Database (SinoMed) from the establishment of the database to the present for studies on the prevalence and influencing factors of career plateau in nurses. Two researchers will independently conduct literature screening and data extraction. They will evaluate the quality of the included studies using the Joanna Briggs Institute critical appraisal tool and the mixed-methods appraisal tool. If possible, we will conduct a meta-analysis, which is according to the data heterogeneity. If the heterogeneity is too large to be resolved, we will present them narratively and descriptively according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses criteria, summarise status and influencing factors of the career plateau in nurses. ETHICS AND DISSEMINATION: No formal ethics approval is required for this protocol and no primary data will be collected. The results will be disseminated through peer-reviewed journals and presented at conferences. PROSPERO REGISTRATION NUMBER: CRD42024545439.
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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 | 0.100 | 0.142 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.022 | 0.027 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.058 | 0.006 |
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".