Mini Med School: Knowledge and Resources for Underrepresented in Medicine Youth
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Physician demographics in North America do not yet reflect the diversity of the communities they serve, accounted to systemic barriers targeting underrepresented in medicine (URiM) groups. URiM medical graduates are more likely to pursue generalist specialties, including family medicine. Mini Med Schools (MMSs) are pathway programs intended to motivate URiM youth to pursue medicine. A gap in literature exists regarding the potential of MMSs to provide youth with useful information. We examined the extent to which youth reported a change in knowledge about medicine as a career before and after attending an MMS. METHODS: Asclepius Medical Camp for Youth is a weeklong MMS for high school students, held at one Canadian university. In 2022, 50 youth participants were invited to complete surveys and quizzes measuring their knowledge about pursuing a career in medicine. RESULTS: The mean self-reported knowledge differed significantly precamp (n=34, M=5.87/10, SD=1.9) versus postcamp (n=26, M=8.28/10, SD=1.4; t[35]=7.07, P<.05). Likewise, participants' scores demonstrated a significant difference in mean scores precamp (n=43, M=7.12, SD=2.39) versus postcamp (n=39, M=9.31, SD=1.13; t[42]=5.08, P<.05). CONCLUSIONS: These findings highlight MMSs as a promising strategy to provide knowledge about medical careers beyond instilling motivation. By both inspiring and informing URiM youth, the long-term outcome of diversifying medicine may be achieved.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".