Culinary Medicine and Teaching Kitchens: Dietitians Leading Innovative Nutrition Training for Physicians
Bibliographic record
Abstract
The objective of this evaluation was to determine the impact of a pop-up Teaching Kitchen (TK) at a national cardiovascular conference. The 60-minute session was hosted in a hotel conference room and led by two registered dietitians. Participants prepared 12 recipes, enjoyed a family-style meal, and explored nutrition behaviour change strategies for patients. Using Likert-scaled and open-ended questions, pre-/post-online surveys assessed change in perceived nutrition counselling skills, attitudes, and confidence; post-survey also assessed effectiveness of session components and further training needs. Pre-survey response was 72% (18/25). Twenty-one participants attended the event (14 pre-registrants, six from waitlist, and five drop-ins); 81% completed the post-survey. Positive shifts were reported in nutrition competence, particularly attitudes towards using recipes in nutrition counselling, and increased skills and confidence discussing eating on a budget and SMART (Specific, Measurable, Achievable, Relevant, and Time-Bound) goal setting with patients. Components of the TK session that enhanced nutrition competence were key patient messages and the shared meal. Preparing and eating together in a hands-on format was most enjoyable. Promoting healthy eating behaviours requires understanding the complexity of individual and societal food literacy. With high physician interest, dietitians are well positioned to deliver culinary medicine interventions and support physicians' confidence in health promotion and chronic disease prevention and management.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".