Cooking skills in relation to diet quality in children: a cross-sectional analysis
Bibliographic record
Abstract
Increasing childhood obesity rates and poor eating habits have led health professionals to explore strategies to improve children's dietary intake. One such strategy is the promotion of cooking skills. This study examined the cross-sectional association between children's cooking skills and diet quality, using data from the Guelph Family Health Study. Data from 81 children (mean age of 9.1 years; 74.1% White) from 68 families were included. Children self-reported their cooking skills using items from the Tool for Food Literacy Assessment in Children, and parents reported their children's dietary intake using the Automated Self-Administered 24-Hour Dietary Assessment Tool (ASA-24) from which diet quality was determined using the Healthy Eating Index (HEI-2020). Linear regression models with generalized estimating equations were used to explore associations between child cooking skills and HEI-2020 total and component scores, adjusted for child age and sex, household income, and intervention status. Mean child cooking skills score (out of a maximum of 4) was 3.08 ± 0.68, and mean HEI-2020 total score (out of a maximum of 100) was 60 ± 12.99. Child cooking skills score was not significantly associated with HEI-2020 total score (ß = −0.78, 95% CI (−4.88, 3.33 p = 0.71), but significantly associated with HEI-2020 Total Vegetables component score (ß = 0.47, 95% CI (0.05, 0.89, p = 0.03). These findings highlight the need for more research, especially longitudinal, to help determine the association between child cooking skills and their diet quality.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".