Mini-med school for Aboriginal youth: experiential science outreach to tackle systemic barriers
Bibliographic record
Abstract
INTRODUCTION: Addressing systemic barriers experienced by low-income and minority students to accessing medical school, the University of Calgary's Cumming School of Medicine has spearheaded a year-round, mini-med school outreach initiative for Aboriginal students. METHOD: Junior and senior high school youth generally attend the half-day program in classes or camps of 15-25, breaking into small groups for multisession activities. Undergraduate medical education students mentor the youth in stations offering experiential lessons in physical examination, reading x-rays, and anatomy. All resources from the medical school are offered in-kind, including a pizza lunch at midday, whereas community partners organize transportation for the attendees. RESULTS: Opening the medical school and its resources to the community offers great benefits to resource-constrained schools often limited in terms of science education resources. The model is also an effort to address challenges among the medical professions around attracting and retaining students from underserved populations. CONCLUSION: The prospect of increasing admission rates and successful completion of medical education among students from marginalized communities poses a real, though difficult-to-measure, possibility of increasing the workforce most likely to return to and work in such challenging contexts. A mini-medical school for Aboriginal youth highlights mutual, long-term benefit for diverse partners, encouraging medical educators and community-based science educators to explore the possibilities for deepening partnerships in their own regions.
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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 | 0.003 | 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 | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".