Mentorship in nursing academia: a systematic review protocol
Bibliographic record
Abstract
BACKGROUND: Mentorship is perceived as vital to attracting, training, and retaining nursing faculty members and to maintaining high-quality education programs. While there is emerging evidence to support the value of mentorship in academic medicine, the extant state of the evidence for mentorship in nursing academia has not been established. We describe a protocol for a mixed-methods systematic review to critically appraise the evidence for mentorship in nursing academia. METHODS: Studies examining the effectiveness of mentorship interventions with nursing faculty who teach in registered nursing education programs will be included. Mentee, mentor, and nursing education institutional outcomes will be explored. Quantitative, qualitative, and mixed method studies will be eligible for inclusion, without restrictions on publication status, year of publication, or language. We will search electronic databases (for example, MEDLINE, CINAHL, ERIC) and gray literature (for example, conference proceedings, key journals, relevant organizational websites) for relevant citations. Using pilot-tested screening and data extraction forms, two reviewers will independently review the studies in three steps: (1) abstract/title screening, (2) full-text screening of accepted studies, and (3) data extraction of accepted studies. Studies will be aggregated for meta-synthesis (qualitative) and meta-analysis (quantitative), should the data permit. DISCUSSION: This study is the first systematic review of existing global evidence for mentorship in nursing academia. It will help identify key evidence gaps and inform the development and implementation of mentorship interventions. The mentorship outcomes that result from this review could be used to guide the practice of mentorship to increase positive outcomes for nursing faculty and the students they teach and ultimately effect improvements for the patients they care for. This review will also identify key considerations for future research on mentorship in nursing academia and the enhancement of nursing science.
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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 | 0.167 | 0.136 |
| Meta-epidemiology (narrow) | 0.007 | 0.008 |
| Meta-epidemiology (broad) | 0.019 | 0.015 |
| Bibliometrics | 0.020 | 0.019 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.078 | 0.016 |
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".