Clinical Nurse Educators’ Job Satisfaction and Turnover Intentions: Protocol for a Scoping Review
Bibliographic record
Abstract
BACKGROUND: Clinical Nurse Educators (CNEs) play a critical role in supporting the recruitment, retention, and professional development of staff nurses in acute care hospital settings. In the context of the COVID-19 pandemic, which has exacerbated global nursing shortages, CNEs are reporting high levels of burnout associated with unpredictable work expectations and nursing workforce challenges. However, the factors that influence job satisfaction and turnover intentions among CNEs are not well understood. OBJECTIVE: The objective of this scoping review is to provide a comprehensive overview of existing literature that focuses on job satisfaction and turnover intentions among CNEs within the acute care setting. METHODS: Joanna Briggs Institute and PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines will be used for this scoping review. Using a predetermined query of keywords, a comprehensive search will be conducted in SCOPUS, CINAHL, MEDLINE, PsycINFO, Web of Science, citation searching, and gray literature sources to identify studies published between January 2013 and August 2024. This review will consider all literature sources that explore the concept of job satisfaction and turnover intentions of CNEs in the context of acute care settings such as hospitals and short-stay units. Two independent reviewers will screen and select studies, and data extraction will be completed using Covidence (Veritas Health Innovation) systematic review software to gather specific data from each literature source. The results will be synthesized to map out themes within existing literature and to identify areas for further research. Supported by tables and graphs, thematic content analysis will be used to provide an extensive overview of what is known about the topic of interest. RESULTS: The online database literature search was initiated in January 2025, with a total of 4306 records identified during the initial search. After removing duplicates, 2354 unique records will be screened based on title and abstract. As of February 2025, title and abstract screening is currently underway, with a subsequent full-text screening and gray literature search to follow. We expect data analysis to be completed in April 2025 and results published in May 2025. CONCLUSIONS: Rather than evaluating the significance of findings in individual studies, this review will contribute new insights to the existing literature and identify areas for further research. TRIAL REGISTRATION: OSF Registries osf.io/ectu7; https://osf.io/ectu7. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/66712.
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 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 | 0.092 | 0.087 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.013 | 0.015 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.107 | 0.018 |
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".