Epistemology, culture, justice and power: non‐bioscientific knowledge for medical training
Bibliographic record
Abstract
CONTEXT: While medical curricula were traditionally almost entirely comprised of bioscientific knowledge, widely accepted competency frameworks now make clear that physicians must be competent in far more than biomedical knowledge and technical skills. For example, of the influential CanMEDS roles, six are conceptually based in the social sciences and humanities (SSH). Educators frequently express uncertainty about what to teach in this area. This study concretely identifies the knowledge beyond bioscience needed to support the training of physicians competent in the six non-Medical Expert CanMEDS roles. METHODS: We interviewed 58 non-clinician university faculty members with doctorates in over 20 SSH disciplines. We abstracted our transcripts (meaning condensation, direct quotations) resulting in approximately 300 pages of data which we coded using top-down (by CanMEDS role) and bottom-up (thematically) approaches and analysed within a critical constructivist framework. Participants and clinicians with SSH PhDs member-checked and refined our results. RESULTS: Twelve interrelated themes were evident in the data. An understanding of epistemology, including the constructed nature of social knowledge, was seen as the foundational theme without which the others could not be taught or understood. Our findings highlighted three anchoring themes (Justice, Power, Culture), all of which link to eight more specific themes concerning future physicians' relationships to the world and the self. All 12 themes were cross-cutting, in that each related to all six non-Medical Expert CanMEDS roles. The data also provided many concrete examples of potential curricular content. CONCLUSIONS: There is a definable body of SSH knowledge that forms the academic underpinning for important physician competencies and is outside the experience of most medical educators. Curricular change incorporating such content is necessary if we are to strengthen the non-Medical Expert physician competencies. Our findings, particularly our cross-cutting themes, also provide a pedagogically useful mechanism for holistically teaching the underpinnings of physician competence. We are now implementing our findings into medical curricula.
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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 | 0.026 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.048 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".