Group mentorship for undergraduate medical students—a systematic review
Bibliographic record
Abstract
INTRODUCTION: Mentoring has become a prevalent educational strategy in medical education, with various aims. Published reviews of mentoring report very little on group-based mentorship programs. The aim of this systematic review was to identify group-based mentorship programs for undergraduate medical students and describe their aims, structures, contents and program evaluations. Based on the findings of this review, the authors provide recommendations for the organization and assessment of such programs. METHODS: A systematic review was conducted, according to PRISMA guidelines, and using the databases Ovid MEDLINE, EMBASE, PsycINFO and ERIC up to July 2019. Eight hundred abstracts were retrieved and 20 studies included. Quality assessment of the quantitative studies was done using the Medical Education Research Study Quality Instrument (MERSQI). RESULTS: The 20 included studies describe 17 different group mentorship programs for undergraduate medical students in seven countries. The programs were differently structured and used a variety of methods to achieve aims related to professional development and evaluation approaches. Most of the studies used a single-group cross-sectional design conducted at a single institution. Despite the modest quality, the evaluation data are remarkably supportive of mentoring medical students in groups. DISCUSSION: Group mentoring holds great potential for undergraduate medical education. However, the scientific literature on this genre is sparse. The findings indicate that group mentorship programs benefit from being longitudinal and mandatory. Ideally, they should provide opportunities throughout undergraduate medical education for regular meetings where discussions and personal reflection occur in a supportive environment.
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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 | 0.021 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".