The Need for Social Accountability in Medical School Education: a Tale of Five Students’ Integration into Vancouver’s Downtown Eastside
Bibliographic record
Abstract
AbstractMedical educators are recognizing that social accountability is a tenet of Canadian medical education, yet it is a difficult concept to teach didactically. Accumulating evidence supports the integration of social accountability into the medical curriculum through community involvement. Fortunately, the University of British Columbia Faculty of Medicine enables students to pursue community learning as part of its curriculum; and we, five medical students, benefited from that opportunity. This commentary will promote the importance of teaching social accountability in medical schools through community-based learning based on available literature and our personal experience with Vancouver’s Downtown Eastside (DTES). RésuméLes professeurs de médecine reconnaissent que la responsabilité sociale est un pilier de l’éducation médicale canadienne; néan- moins, c’est un concept difficile à enseigner didactiquement. De plus en plus de preuves appuient l’intégration de la responsabilité sociale au curriculum médical à travers l’engagement communautaire. Heureusement, la Faculté de Médecine de l’Université de la Colombie-Britannique permet aux étudiants de participer à l’apprentissage par engagement communautaire en tant que composante du curriculum; nous, cinq étudiants en médecine, avons pu profiter de cette opportunité. Ce commentaire va promouvoir l’importance d’enseigner la responsabilité sociale dans les écoles de médecine par l’intermédiaire de l’apprentissage par engagement communau- taire, basé sur la littérature disponible et notre expérience personnelle avec le quartier de Downtown Eastside de Vancouver (DTES).
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.033 | 0.007 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".