Mentoring Matters: Evaluating The Black Physicians of Canada Mentorship Program
Bibliographic record
Abstract
Abstract Purpose Black individuals face significant barriers in medicine, contributing to their underrepresentation as physicians and emphasizing the need for systemic change. From admissions processes to program design, medical schools often uphold outdated racial practices that disadvantage Black learners. Increased representation in medical schools benefits learners and improves care for diverse patient populations. Mentorship has proven essential in fostering success in higher education and can mitigate barriers to career progression. However, many Black learners face barriers to accessing quality mentorship despite its proven benefits in fostering equitable opportunities and career progression. The Scarborough Charter outlined 58 Canadian institutions committed to advancing Black inclusion in higher education through mentorship and accountability measures. In alignment with this goal, the Black Physicians of Canada (BPC) launched a racially concordant mentorship program. This study aimed to explore participants’ experiences and provide recommendations for future program iterations. Methods This study employed a convergent triangulation mixed methods design. Both quantitative and qualitative data were collected. The Yukawa Mentorship Evaluation Tool was used to measure program effectiveness. Descriptive analyses were conducted by a member of the research team. Data was coded by two members of the research team and findings were audited for consistency. Results A total of 51 participants (27 mentors, 24 mentees) completed the survey, and 13 (7 mentors, 6 mentees) participated in semi-structured interviews. Five themes emerged: Mentorship Characteristics, Program Administration, Perceived Program Benefits, Barriers to Mentorship, and Recommendations for Improvement. Conclusion The BPC mentorship program represents a historic step toward addressing unmet needs of Black medical residents in Canada. Participants expressed high satisfaction and highlighted areas for improvement. Racially concordant mentorship was seen as particularly valuable in addressing unique challenges faced by Black learners. The findings from this study provide critical insights into best practices for future mentorship programs, advancing diversity and equity in medicine while supporting Black learners’ success.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".