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 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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".