Predictors of Food Skills in University Students
Bibliographic record
Abstract
Purpose: To determine predictors of food skills in university students, specifically, the relative effects of a food and nutrition (FN) course; sex, age, and body mass index; food-related behaviours in the parental home; and food-related behaviours in university. Methods: Undergraduate students (n = 30 310) at Western University were invited to complete an online cross-sectional survey that assessed 7 components of food skills, from mechanical (e.g., peeling/chopping) to conceptual (e.g., weekly meal planning). The primary outcome measure was Total Food Skills Score (TFSS). All variables that were statistically associated with TFSS (P < 0.05) were analyzed hierarchically in 4 regression models. Results: The sample was comprised of 3354 students living independently for 2.6 ± 1.1 years. Students who had taken an FN course had higher food skills than those who had not (B = 30.72; P < 0.001), and this relationship remained significant through all subsequent models. The strongest predictor of food skills was meal preparation as a teen (B = 25.66; P < 0.001). Frequency of using a grocery list, packing a lunch, and time spent preparing meals on weekends were positively associated with food skills (P < 0.001), whereas frequency of buying pre-prepared meals was negatively associated with food skills (P < 0.001). Conclusions: Food skill development should occur well before young adults begin living independently.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".