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Record W4392032523 · doi:10.3390/nu16050603

Culinary Medicine or Culinary Nutrition? Defining Terms for Use in Education and Practice

2024· article· en· W4392032523 on OpenAlexaffabout
Sharon Croxford, Emma Stirling, Julia MacLaren, John Wesley McWhorter, Lynn Frederick, Olivia Thomas

Bibliographic record

VenueNutrients · 2024
Typearticle
Languageen
FieldHealth Professions
TopicObesity and Health Practices
Canadian institutionsAlberta Health Services
Fundersnot available
KeywordsNutrition EducationMedical educationBest practiceWork (physics)MedicinePsychologyGerontologyPolitical science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.309
Threshold uncertainty score0.467

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.139
GPT teacher head0.535
Teacher spread0.396 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations16
Published2024
Admission routes2
Has abstractyes

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