Mentoring for Admission and Retention of Black Socio-Ethnic Minorities in Medicine: A Scoping Review
Bibliographic record
Abstract
Purpose: Despite numerous mentoring strategies to promote academic success and eligibility in medicine, Black students remain disproportionately underrepresented in medicine. Therefore, we conducted a scoping review to identify the mentoring practices available to Black pre-medical students, medical students and medical residents, specifically the mentoring strategies used, their application, and their evaluation. Method: Between May 2023 and October 2023, the authors conducted a literature review. Studies that described a mentoring strategy applied among Black learners were eligible for inclusion, and all years of publication were included. Two reviewers screened each article using the Covidence tool, and conflicts were resolved by a third author. All reviewers extracted the data to summarize the various mentoring practices. Results: After screening 6292 articles, 42 articles met the criteria for full review. Of these, 14 studies were included in the study. Mentoring practices for Black students included peer mentoring, dyad mentoring, and group mentoring. Mentoring was typically offered through discussion groups, educational internships, and didactic activities. Evaluation of mentoring programs took into account (1) pass rates on medical exams (eg, MCAT, Casper), (2) receipt of an invitation to a medical school admissions interview, (3) successful match to a competitive residency program, and (4) a mentee's report of the overall experience and effectiveness of the program. Conclusion: This review is the first, to our knowledge, to focus on mentoring strategies implemented among Black learners in medicine. The results will inform mentoring strategies adapted for Black learners and will therefore address the underrepresentation of Black students in medicine.
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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 | 0.017 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".