Evaluation of a College-Level Nutrition Course With a Teaching Kitchen Lab
Bibliographic record
Abstract
OBJECTIVE: To evaluate the impact of a college nutrition course with a teaching kitchen lab on students' attitudes, self-efficacy, and behaviors about healthful eating and cooking. METHODS: Preintervention and postintervention design, and anonymous online survey of sociodemographic information and students' attitudes and self-efficacy about consuming fruits, vegetables, and whole grains and about cooking, self-reported intake, and cooking behaviors. RESULTS: Two-hundred and fourteen participants enrolled in the study during 5 semesters. Of these, 171 (80%) had complete pretest and posttest data. Attitudes and self-efficacy scores about consuming fruits, vegetables, whole grains, and cooking were significantly higher in the posttest (vs pretest; all P < 0.0001). Self-reported intake of fruits (P < 0.0001) and vegetables (P = 0.0006) also increased. Cooking frequency increased (P < 0.0001), skipping meals frequency decreased (P < 0.0001), whereas no significant changes were observed for eating out, take-out, or premade meals frequency. CONCLUSIONS AND IMPLICATIONS: A college nutrition course with a teaching kitchen lab could improve healthful eating and promote cooking in young adults.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".