A Conceptual Model for Teaching Social Responsibility and Health Advocacy: An Ambulatory/Community Experience (ACE)
Bibliographic record
Abstract
Background: At a macro level, Social Responsibility and Social Accountability are explicit priorities for medical schools in Canada and internationally, although the advancement of this vision is still developing. At a micro level, Health Advocacy is important for physicians-in-training as well as practicing physicians. The conceptual model being proposed is that Social Responsibility is connected to mastering Health Advocacy. The University of Toronto Faculty of Medicine has 16 years of experience through a mandatory 4th year clerkship course entitled the Ambulatory/Community Experience (ACE) which from inception emphasized Social Responsibility and Health Advocacy. The objective of this retrospective study was to provide a model to support the acquisition of Health Advocacy and the development of socially responsible medical students.Methods: A conceptual model with three distinct elements: 1) ambulatory/community placements, 2) individual pedagogical approaches and 3) narrative, reflective assignments was applied.Results: The three elements of the model, all based on the five ACE learning domains (objectives) and embedded in CanMEDS type competencies, are effective and appear to support achievement of competency in Health Advocacy.Conclusion: A model which includes vetted ambulatory/community placements, individual pedagogical approaches, and narrative reflective assignments based on objectives with a Health Advocate perspective appears to encourage Social Responsibility in medical students.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".