Exploring possibilities of harmonising social justice with medical education through the use of CanMeds and AfriMeds when engaging in discipline integration
Bibliographic record
Abstract
Medical curricula are largely content heavy and grossly overloaded but focus primarily on medical and biomedical sciences. It has been argued by authors such as Gukas and Filies that well-balanced professionals are seldom produced by such content heavy curricula. The incorporation of social justice principles in medical curricula is vital in promoting the production of well-balanced and competent healthcare professionals, as called for in the CanMeds/AfriMeds frameworks. However, the World Federation for Medical Education issued a consensus statement asserting that medical students in the USA and Canada receive little to no formal training and teachings as far as social justice is concerned. In this paper it is asserted that medical students in South Africa are no exception to such consensus. It is further asserted in this paper that if one begins to examine principles of CanMeds/AfriMeds, entry points for the insertion of social justice principles becomes a possibility without having to further overload an overloaded curriculum. In essence, adopting and promoting roles of CanMeds/AfriMeds such as professional, collaborator and scholar enhance a non-hierarchical style and environment of teaching medical students. This new style and environment of learning are shown in this paper to enable an insertion of social justice principles in a medical curriculum in instances where such insertion may otherwise have been impossible.
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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 | 0.017 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".