An Active Learning Model for Promoting Healthy Cooking and Dietary Strategies Among South Asian Children: A Proof-of-Concept Study
Bibliographic record
Abstract
Background/Objectives: South Asian children living in Canada have a higher prevalence of cardiovascular disease risk factors compared to their non-South Asian counterparts, and poor dietary habits may contribute to this health disparity. Methods: This study uses a pre–post intervention design to examine the impact of a family-focused, “hands-on” cooking workshop on improving three cooking and dietary strategies: (1) using healthy cooking techniques, (2) practicing portion control, and (3) making healthy substitutions. We recruited 70 South Asian parent–child dyads (n = 140) across four elementary schools in Surrey, British Columbia. The 90 min workshop includes a didactic segment on healthy food preparation and dietary strategies, followed by an interactive cooking session where participants make a healthier version of a traditional Punjabi dish. Results: Among the three dietary strategies measured, both children and parents increased their frequency of using healthy cooking techniques (child p = 0.02; parent p < 0.001) and practicing portion control (child p < 0.001; parent p = 0.02). No changes were reported by either group for making healthy substitutions. Conclusions: Findings suggest that educational approaches that engage the family as a unit and encourage active participation are associated with improvements in cooking and dietary strategies in the South Asian community.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".