What motivates medical students to learn about traditional medicine? A qualitative study of cultural safety in Colombia
Bibliographic record
Abstract
OBJECTIVES: This study explored motivation dynamics of medical students engaging with traditional medicine in Colombia. METHODS: We conducted a qualitative descriptive study as part of a larger participatory research effort to develop a medical education curriculum on cultural safety. Four final-year medical students participated in a five-month program to strengthen knowledge of traditional medicinal plants with schoolchildren in Cota, a municipality outside Bogota with a high proportion of traditional medicine users. Students and schoolteachers co-designed the program aimed to promote the involvement of school children with traditional medicine in their community. The medical students shared written narratives describing what facilitated their work and discussed experiences in a group session. Inductive thematic analysis of the narratives and discussion derived categories of motivation to learn about traditional medicine. RESULTS: Five key learning dynamics emerged from the analysis: (1) learning from/with communities as opposed to training them; (2) ownership of medical education as a result of co-designing the exercise; (3) rigorous academic contents of the program; (4) lack of cultural safety training in university; and (5) previous contacts with traditional knowledge. CONCLUSIONS: We identified potential principles for engaged cultural safety training for medical students. We will use these in our larger training program. Our results may be relevant to other researchers and medical educators wanting to improve the interaction of medical health professionals in multicultural settings with people and communities who use traditional medicine. We expect these professionals will be better prepared to recognize and address intercultural challenges in their clinical practice.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".