Culinary Medicine or Culinary Nutrition? Defining Terms for Use in Education and Practice
Bibliographic record
Abstract
Examination of how terms such as culinary nutrition, culinary nutrition science, culinary medicine, culinary nutrition professional, culinary nutrition intervention, culinary nutrition activity, and culinary nutrition competency are used in practice, and the creation of consensus definitions will promote the consistent use of these terms across work areas and disciplines. Thirty leading practitioners, academics, and researchers in the fields of food and nutrition across Australia, the United States, Canada, United Kingdom, Europe, and Asia were approached by investigators via email to submit definitions of key terms using a Qualtrics survey link. Further participants were reached through snowball recruitment. Initial emails were sent in October and November 2021 with subsequent reminders between November 2021 and March 2022. Two researchers undertook content analysis of the text answers for each of the terms and generated definitions for discussion and consensus. Thirty-seven participants commenced the survey and twenty-three submitted one or more definitions. Agreed definitions fell into two categories: practice concepts and practitioners. Further discussion amongst investigators led to the creation of a visual map to demonstrate the interrelationship of terms. Culinary nutrition science underpins, and interprofessional collaboration characterizes practice in this area, however, further work is needed to define competencies and model best 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 | 0.031 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.003 | 0.027 |
| Scholarly communication | 0.009 | 0.019 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".