Educating Medical Students’ “Hearts and Minds”: A Humanities-Informed Cultural Immersion Program in Indigenous Experiential Community Learning
Bibliographic record
Abstract
Well-documented disparities in health status persist between Indigenous and non- Indigenous people in Canada. Medical schools have a responsibility to address underlying causes of these inequities, in part by developing future physicians’ cultural humility and their capacities in cultural safety by increasing critical anti- racism knowledge and understandings about First Nations, Inuit, and Métis Peoples. Undergraduate medical education relies heavily on, among other pedagogies, experiential learning. Moreover, a growing body of research is evidencing the value of applying humanities-informed approaches to medical education in order to produce “better doctors” (i.e., physicians who are more empathetic, compassionate, and attuned to wholistic orientations to patient wellness). The combined impact of these two approaches (experiential learning and humanities-informed pedagogies) on medical students’ development of cultural humility and capacities in cultural safety with Indigenous Peoples is unknown. This paper describes how the First Nations Community Education Program—an innovative humanities-informed Indigenous cultural immersion program—was developed and implemented as a collaborative project of the Northern Medical Program (itself the result of a partnership between the University of British Columbia’s Faculty of Medicine and the University of Northern British Columbia), the First Nations Health Authority, and Northern Health. The paper also documents impacts of the program and provides a resource for other medical education programs considering similar initiatives focused on cultural humility or the Truth and Reconciliation Commission’s Calls to Action.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".