Culinary medicine course: qualitative assessment of an innovative pedagogical approach
Bibliographic record
Abstract
OBJECTIVE: While nutrition plays a major role in health, medical students have generally not received adequate nutritional education, lack confidence in their nutritional knowledge and feel unqualified to offer nutrition advice to future patients. Culinary medicine programmes have been developed to address this gap and employ an active learning approach that integrates medical and nutritional learning with the acquisition of culinary competencies and skills. This study aimed to qualitatively evaluate the Université Laval culinary medicine course based on students' experiences of the course structure, active learning approach and its influence on their lifestyle, clinical practice and future approach to nutrition as physicians. DESIGN: Discussion groups were conducted. Thematic content analysis of discussion group data was performed. SETTING: A first French-language culinary medicine course was developed and pilot tested at Université Laval. The curriculum of this course combined online training videos on medical and nutritional concepts, hands-on cooking sessions and the realisation of a collaborative project. PARTICIPANTS: 12). RESULTS: Students valued the course's innovative active learning approach, noting improvements in their diet, nutrition and cooking knowledge, skills, self-efficacy and confidence. They also developed greater critical thinking regarding nutrition and recognised their role in collaborating with dietitians. CONCLUSION: The culinary medicine course demonstrated prospective benefits for medical students, potentially improving their personal and future patients' health and the integration of nutrition into medical education and 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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".