Projects in medical education: "Social Justice in Medicine" a rationale for an elective program as part of the medical education curriculum at John A. Burns School of Medicine.
Bibliographic record
Abstract
BACKGROUND: Research has shown that cultural competence training improves the attitudes, knowledge, and skills of clinicians related to caring for diverse populations. Social Justice in medicine is the idea that healthcare workers promote fair treatment in healthcare so that disparities are eliminated. Providing students with the opportunity to explore social issues in health is the first step toward decreasing discrimination. This concept is required for institutional accreditation and widely publicized as improving health care delivery in our society. METHODS: A literature review was performed searching for social justice training in medical curricula in North America. RESULTS: Twenty-six articles were discovered addressing the topic or related to the concept of social justice or cultural humility. The concepts are in accordance with objectives supported by the Future of Medical Education in Canada Report (2010), the Carnegie Foundation Report (2010), and the LCME guidelines. DISCUSSION: The authors have introduced into the elective curriculum of the John A. Burns School of Medicine a series of activities within a time span of four years to encourage medical students to further their knowledge and skills in social awareness and cultural competence as it relates to their future practice as physicians. At the completion of this adjunct curriculum, participants will earn the Dean's Certificate of Distinction in Social Justice, a novel program at the medical school. It is the hope of these efforts that medical students go beyond cultural competence and become fluent in the critical consciousness that will enable them to understand different health beliefs and practices, engage in meaningful discourse, perform collaborative problem-solving, conduct continuous self-reflection, and, as a result, deliver socially responsible, compassionate care to all members of society.
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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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".