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Record W2807869817 · doi:10.3148/cjdpr-2018-012

A Canadian University “Understanding Foods” Course Improves Confidence in Food Skills and Food Safety Knowledge

2018· article· en· W2807869817 on OpenAlexaffvenueabout
J Bertrand, Alison Crerar, Janis Randall Simpson

Bibliographic record

VenueCanadian Journal of Dietetic Practice and Research · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Safety and Hygiene
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsFood safetyCourse (navigation)Environmental healthMedicineMedical educationBusinessEngineering

Abstract

fetched live from OpenAlex

The impact of a hands-on foods course on undergraduate students' food skills was examined at the University of Guelph. For a convenience sample, first- and second-year students (n = 47, 87% female) registered in the "Understanding Foods" course were recruited to participate in a survey administered on Qualtrics at the beginning of the semester and again at the end of the semester. Participants were asked questions related to demographics and food habits; additional questions on food skills, in Likert-scale format, included confidence in food preparation, food safety knowledge, and grocery shopping habits. Subscales were combined for an overall Food Skills Questions (FSQ) score and differences were determined by paired t tests. Overall, significant (P < 0.05) improvements were observed related to students' confidence and food safety knowledge scores as well as the overall FSQ score. Students, however, rated their personal eating habits more poorly (P < 0.05) at the end of the semester. As a lack of food skills is often considered a barrier for healthy eating among students, these results signify the importance of a hands-on introductory cooking course at the undergraduate level.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.854
Threshold uncertainty score0.890

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
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 head0.072
GPT teacher head0.314
Teacher spread0.241 · 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 designOther design
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
Published2018
Admission routes3
Has abstractyes

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