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Record W3133287327 · doi:10.36834/cmej.70611

Interprofessional culinary education workshops at the University of Saskatchewan

2021· article· en· W3133287327 on OpenAlexaffvenueabout
Jessica Lieffers, Erin Wolfson, Gabilan Sivapatham, Astrid Lang, Alexa McEwen, Marcel D’Eon, Carol J. Henry

Bibliographic record

VenueCanadian Medical Education Journal · 2021
Typearticle
Languageen
FieldHealth Professions
TopicDietetics, Nutrition, and Education
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMedical educationExperiential learningInterprofessional educationStatement (logic)PsychologyMedicineHealth carePedagogyPolitical science

Abstract

fetched live from OpenAlex

Implication Statement If you want to offer your students an enjoyable and worthwhile interprofessional activity to learn about issues in community nutrition, your university can cook up these interprofessional culinary education workshops. Start with a few enthusiastic students from various health professional programs who can organize, promote, and lead. Include faculty and/or staff to support the students and apply for internal funding. Find workshop facilitators (e.g., chefs), and arrange for program evaluation. It is best to choose workshop topics and themes relevant to your local situation. Ensure workshops are structured to facilitate cooperative and experiential learning. Students will find these sessions informative, practical, and enjoyable. Énoncé des implications de la recherche Les ateliers culinaires sont une activité interprofessionnelle agréable et intéressante que votre université peut proposer aux étudiants qui souhaitent se familiariser avec les enjeux de la nutrition communautaire. Il suffit de réunir, pour commencer, quelques étudiants motivés issus de divers programmes de santé pour organiser, promouvoir et diriger les ateliers. Il s'agit ensuite de trouver les enseignants ou le personnel pour les soutenir, et de s'assurer d'un financement interne. Il faut ensuite trouver des animateurs d'ateliers (par exemple, un chef) et planifier l'évaluation du programme. Il est préférable d'axer les ateliers sur des thèmes adaptés à votre milieu. Les ateliers doivent être structurés de manière à faciliter l'apprentissage coopératif et expérientiel. Les étudiants trouveront ces séances instructives, pratiques et agréables.

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
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 score0.398
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0980.000

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 head0.021
GPT teacher head0.356
Teacher spread0.336 · 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.

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

Citations8
Published2021
Admission routes3
Has abstractyes

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