A Scoping Review of Health Care Faculty Mentorship Programs in Academia: Implications for Program Design, Implementation, and Outcome Evaluation
Bibliographic record
Abstract
INTRODUCTION: Formal mentoring programs have direct benefits for academic health care institutions, but it is unclear whether program designs use recommended components and whether outcomes are being captured and evaluated appropriately. The goal of this scoping review is to address these questions. METHODS: We completed a literature review using a comprehensive search in SCOPUS and PubMed (1998-2019), a direct solicitation for unpublished programs, and hand-searched key references, while targeting mentor programs in the United States, Puerto Rico, and Canada. After three rounds of screening, team members independently reviewed and extracted assigned articles for 40 design data items into a comprehensive database. RESULTS: Fifty-eight distinct mentoring programs were represented in the data set. The team members clarified specific mentor roles to assist the analysis. The analysis identified mentoring program characteristics that were properly implemented, including identifying program goals, specifying the target learners, and performing a needs assessment. The analysis also identified areas for improvement, including consistent use of models/frameworks for program design, implementation of mentor preparation, consistent reporting of objective outcomes and career satisfaction outcomes, engagement of program evaluation methods, increasing frequency of reports as programs as they mature, addressing the needs of specific faculty groups (eg, women and minority faculty), and providing analyses of program cost-effectiveness in relation to resource allocation (return on investment). CONCLUSION: The review found that several mentor program design, implementation, outcome, and evaluation components are poorly aligned with recommendations, and content for URM and women faculty members is underrepresented. The review should provide academic leadership information to improve these discrepancies.
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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 | 0.173 | 0.384 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.035 | 0.040 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".