What do participants value in a diversity mentorship program? Perspectives from a Canadian medical school
Bibliographic record
Abstract
Purpose As medical schools become increasingly diverse, there is a growing demand for schools to support their equity-seeking students. At the University of Toronto, the diversity mentorship program (DMP) is a new program created to support equity-seeking and diverse medical students in first- and second-year through didactic lectures, networking opportunities and mentorship from senior clinicians. This article aims to share participant perspectives on how diversity-focused mentorship benefits them, perceived barriers and insights for other institutions developing a similar program. Design/methodology/approach Using a mixed methods design, students and mentors completed semi-structured surveys to assess broad perceptions of their mentorship experiences. Focus groups were conducted with both groups to gain deeper understandings of participants' experiences. The authors performed thematic analysis to identify qualities of successful experiences and barriers to participation. Findings Most mentors and mentees found the DMP helpful and identified five themes contributing to a positive mentorship experience: (1) accessibility, (2) program diversity focus with clear expectations, (3) career guidance, (4) exposure to different perspectives and (5) community and shared identity. Uncertainty on how to help less assertive mentees, mentorship pair discordance where mentees paired by race did not share racial identities and logistical challenges was identified as barriers to maintaining mentoring relationships. Originality/value To the authors’ knowledge, this is the first qualitative study exploring the feelings and impressions of participants in a mentorship program at a medical school addressing the needs of equity-seeking groups. By understanding the characteristics and value of diversity-focused mentorship, this will inform the creation of similar supportive programs across various professional fields at other schools.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.008 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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 teacher head, 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".