Evaluating the Dear MD to Be Podcast as an Equity, Diversity and Inclusion resource: a cross-sectional survey analysis
Bibliographic record
Abstract
Background: Equity-deserving groups are communities marginalized from institutional power by oppressive forces (e.g., racism, sexism, homophobia, ableism). Dear MD to Be is a medical-student-led podcast created to interview physicians of intersectional backgrounds about their institutional experience. This study aims to evaluate the podcast as a tool for knowledge, mentorship, and psychological safety for equity-deserving listeners. Methods: Between February and March 2022, we recruited medical students across all levels of training from English-speaking Canadian medical schools using email listservs and social media. We disseminated a cross-sectional questionnaire assessing demographics, knowledge gained from podcast engagement, attitudes towards podcasts as a tool for mentorship, and psychological/emotional gains from the podcast content. We conducted descriptive and frequency analyses of quantitative data and applied thematic analysis to qualitative data. Results: Thirty-eight individuals completed the entire survey from all levels of training, with 97% self-identifying with at least one equity-deserving group. 100% agreed that the Dear MD to Be podcast was an accessible form of mentorship; participants appreciated self-pacing mentorship and interacting with many narratives. Listeners gleaned lessons about wellness, advocacy work, allyship, cultural imposter syndrome, and navigating discrimination. Furthermore, most listeners felt represented, empowered, and legitimized by podcast content. Conclusions: Podcasts can serve as a medium for accessible equity-centred mentorship. By disseminating multiple underrepresented narratives in medicine, the Dear MD to Be podcast serves as a source of EDI knowledge while contributing to learner safety.
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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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".