Improvement in Nutritional Knowledge Confidence and Food-Agency: Outcomes of the First French-Speaking Culinary Medicine Courses Among Medical Students
Bibliographic record
Abstract
Background: Medical students often lack sufficient nutrition education, leading to confidence gaps and an inability to address this healthcare aspect. Culinary Medicine (CM) courses offer an innovative solution. Methods: We tested the first French-speaking CM courses among 2 groups of second-third year medical students, compared to a control group (CG). The objectives were to assess if an optional CM course could enhance their confidence in both nutritional knowledge and providing nutritional advice, and improve their food agency (CAFPAS: Cooking and Food Provisioning Action Scale). The analysis examines changes in scores by comparing post-session to pre-session questionnaire measurements. Results: Of the 22 CM students and 6 in CG, predominantly aged 20-25 years, Caucasian, and female, the majority (CG = 100%, CM = 86.4%) reported <5 hours of nutrition education. Almost all expressed dissatisfaction with nutrition education provided in medicine, both quantitatively and qualitatively. CM students reported significantly increased confidence in their knowledge and ability to advise about nutrition during the sessions. We also observed improvements in their CAFPAS scores, which measure food agency, while the control group exhibited no change in confidence or CAFPAS scores. Conclusion: The findings highlight CM as practical strategy for integrating nutrition education into medical curricula, offering insights for enhancing future physicians' knowledge.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".