A Canadian University “Understanding Foods” Course Improves Confidence in Food Skills and Food Safety Knowledge
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".