The tip of the iceberg: Generalism in undergraduate medical education, a systems thinking analysis
Bibliographic record
Abstract
PURPOSE: There is a shortage of generalist physicians globally impacting health equity and access to care. An important way in which medical schools can demonstrate social accountability is by graduating learners interested in careers in generalism. While generalism is endorsed as a matter of principle in medical education, how this translates into curricula is less clear. The aim of this study was to identify how generalism is understood and supported by family physician educational leaders in undergraduate medical education (UME) in Canada. METHODS: We conducted a qualitative study, interviewing 38 family medicine leaders in UME across all 17 Canadian medical schools. We examined the data with template analysis, informed by the iceberg model of systems thinking. RESULTS: Four themes were identified: (1) Teaching and learning strategies in support of generalism-a consistent range existed across UME curricula; (2) Curriculum patterns-changes in leadership and curriculum reform created positive or negative feedback loops that promoted or hindered initiatives to support generalism; (3) Curriculum structures-organ-system-based curricula and availability of generalist faculty presented particular challenges to teaching generalist approaches; (4) Mental models and ways of knowing-the preponderance of biomedical frameworks of thinking in curricula unconsciously undermined generalist approaches to patient care. CONCLUSIONS: UME programmes promoted generalism through a range of teaching activities and strategies, but these efforts were countered by curriculum structures and mental models that perpetuate epistemic inequity between biomedical approaches to medical education and generalist models of care. Novel curricular frameworks are needed to align undergraduate programmes' commitment to social accountability with community-based need.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.039 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".