Development of the Cook-EdTM Matrix to Guide Food and Cooking Skill Selection in Culinary Education Programs That Target Diet Quality and Health
Bibliographic record
Abstract
Culinary education programs are generally designed to improve participants’ food and cooking skills, with or without consideration to influencing diet quality or health. No published methods exist to guide food and cooking skills’ content priorities within culinary education programs that target improved diet quality and health. To address this gap, an international team of cooking and nutrition education experts developed the Cooking Education (Cook-EdTM) matrix. International food-based dietary guidelines were reviewed to determine common food groups. A six-section matrix was drafted including skill focus points for: (1) Kitchen safety, (2) Food safety, (3) General food skills, (4) Food group specific food skills, (5) General cooking skills, (6) Food group specific cooking skills. A modified e-Delphi method with three consultation rounds was used to reach consensus on the Cook-EdTM matrix structure, skill focus points included, and their order. The final Cook-EdTM matrix includes 117 skill focus points. The matrix guides program providers in selecting the most suitable skills to consider for their programs to improve dietary and health outcomes, while considering available resources, participant needs, and sustainable nutrition principles. Users can adapt the Cook-EdTM matrix to regional food-based dietary guidelines and food cultures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".