Shifting the paradigm in outreach to under-represented groups
Bibliographic record
Abstract
This article was migrated. The article was marked as recommended. The Community of Support (COS) is a longitudinal and collaborative initiative that enables students who are Indigenous, Black, Filipino, economically disadvantaged, or who self-identify with having a disability to join and receive support at any stage of their medical school journey. The goal of COS is to increase diversity in the fields of research and medicine, as a diverse physician taskforce is essential to meeting the needs of Canada's patient population. Our program supports students at various points in their academic careers, beginning from first year of undergrad to end of PhD and into the workforce. We offer a variety of support systems that aim to address gaps and empower students. Our three-pronged approach provides COS members with support at the levels of i) admissions information, ii) mentorship and experiential opportunities, and iii) application support (including MCAT prep). Over the past 3 years, we have grown to include over 1,100 participants at various stages of their medical school journeys, from first year undergraduate students to university graduates from institution across Canada. As a result, in just three years, we have supported over 80 students with successful admissions to medical school, and alumni from CoS are now represented in 11/15 Canada's medical schools, with a growing number of US schools, such as Yale University, George Washington, Michigan State and Wayne State.
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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 | 0.056 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.025 | 0.043 |
| Scholarly communication | 0.020 | 0.023 |
| Open science | 0.006 | 0.051 |
| Research integrity | 0.011 | 0.019 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".