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 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 | 0.044 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".