Mixed methods systematic review exploring mentorship outcomes in nursing academia
Bibliographic record
Abstract
AIMS: The aim of this study was to report on a mixed methods systematic review that critically examines the evidence for mentorship in nursing academia. BACKGROUND: Nursing education institutions globally have issued calls for mentorship. There is emerging evidence to support the value of mentorship in other disciplines, but the extant state of the evidence in nursing academia is not known. A comprehensive review of the evidence is required. DESIGN: A mixed methods systematic review. DATA SOURCES: Five databases (MEDLINE, CINAHL, EMBASE, ERIC, PsycINFO) were searched using an a priori search strategy from inception to 2 November 2015 to identify quantitative, qualitative and mixed methods studies. Grey literature searches were also conducted in electronic databases (ProQuest Dissertations and Theses, Index to Theses) and mentorship conference proceedings and by hand searching the reference lists of eligible studies. REVIEW METHODS: Study quality was assessed prior to inclusion using standardized critical appraisal instruments from the Joanna Briggs Institute. A convergent qualitative synthesis design was used where results from qualitative, quantitative and mixed methods studies were transformed into qualitative findings. Mentorship outcomes were mapped to a theory-informed framework. RESULTS: Thirty-four studies were included in this review, from the 3001 records initially retrieved. In general, mentorship had a positive impact on behavioural, career, attitudinal, relational and motivational outcomes; however, the methodological quality of studies was weak. CONCLUSION: This review can inform the objectives of mentorship interventions and contribute to a more rigorous approach to studies that assess mentorship outcomes.
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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 | 0.076 | 0.229 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.026 | 0.022 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".