Longitudinal associations between mothers’ and fathers’ food skills and their children's cooking skills
Bibliographic record
Abstract
Since parents serve as role models for their children, their level of food skills may influence their children's cooking skills. Learning how to cook at an early age may lead to better cooking skills and diet quality later in life. The aim of this study was to examine the longitudinal association between parents’ food skills and children's cooking skills using data from the Guelph Family Health Study, a trial of a home-based obesity prevention intervention. Data from 135 families, including 158 children (mean age of 8.9 years; 75.9% White) and their parents (121 mothers and 66 fathers), were included. Parents self-reported their food skills and children self-reported their cooking skills. Linear regression models with generalized estimating equations were used to examine the longitudinal association between parent food skills (as a total food skills score and separately as mechanical and conceptualizing skills) and child cooking skills, adjusted for child age and sex, parent age, household income, and intervention status. Mean overall food skills score (out of a maximum of 4) was 3.65 ± 0.38 for mothers and 3.42 ± 0.52 for fathers; mean cooking skills score for children was 3.36 ± 0.59. Neither mothers’ nor fathers’ food skills were significantly associated with child cooking skills. The findings indicate that despite parents having high overall food skills scores, these food skills do not appear to translate into higher cooking skills scores among their children. Parents may need to directly involve their children in cooking activities to impact their cooking skills. Novelty The findings from this study indicate that despite parents having high overall food skills, neither mothers’ nor fathers’ food skills were significantly associated with their children's cooking skills.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".