Cooking at Home: Correlates of Frequency and Intention in Fifth Graders
Bibliographic record
Abstract
Cooking at home is associated with health benefits, and 10- and 11-year-old children are capable of participating in meal preparation. However, opportunities for children to cook at home have declined. This study aimed to identify determinants of the frequency and the intention to cook at home in fifth graders using the Theory of Planned Behavior as a framework with quantitative methodology. A total of 241 participants across five elementary schools of the Chaudière-Appalaches region (Quebec, Canada) took part in this correlational study. Data were collected via a self-administered questionnaire based on the Theory of Planned Behavior. Regression analyses led to the identification of determinants of frequency and intention to cook at home. More than two-thirds of participants (69%) declared having cooked at home in the past 7 days. Intention was the only significant variable explaining 18% of the variance for frequency. Intention was determined by perceived behavioral control, attitude, descriptive norms, subjective norms, perceived barriers, being a girl, and normative beliefs, which explain 74% of the variance. Whereas other studies aiming at better understanding children's involvement in meal preparation at home focused on self-efficacy for cooking, this study highlights other behavioral determinants. For example, support from parents appears to be crucial to promote this behavior in this age group. Future research and interventions should be oriented toward determinants such as subjective norms and normative beliefs, and focus on children's autonomy.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".