Sparking justice under the aegis of pragmatism: A mentorship program for Black and Indigenous students in Canadian medicine
Bibliographic record
Abstract
While there have been multiple calls to attend to issues of diversity in medical education over past decades, medicine has remained a 'hegemonically white' profession in Canada. Mentorship programs for racialized learners are used as affirmative pathways, but their implementation can become challenging at institutions that work with neoliberal diversity at the expense of equity. There is a paucity of literature on how the advocates of such programs may therefore unwittingly embrace a pragmatic sensibility to negotiate institutional diversity imperatives while also advancing their commitments to equity and inclusion. A mentorship initiative called Sunnybrook Program to Access Research Knowledge (SPARK) for Black and Indigenous medical students was launched at a teaching hospital in Canada in 2021. It provides longitudinal research experience for second year learners so they can compete more effectively for residency placements. Relying on longitudinal critical ethnography, we examine the possibilities and limits the unintentionally adopted pragmatic approach produces for Black and Indigenous medical learners. By privileging diversity and evidence-based medicine, we found, the program made a pragmatic compromise, making the program's implementation possible. It, however, not only mitigated but contributed to the perpetuation of Black and Indigenous learners' epistemic exclusions. This analysis helps us detangle EDI and argue how pragmatically implemented programs deliver on some aspects of EDI more than on others. To foreground equity, especially in this moment of pushback, we propose consciously deploying pragmatism to bridge necessary pragmatic practices in the present with longer-term actions to dismantle entrenched inequities and thereby facilitate more effective EDI programs.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.060 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".