Cooking and Its Impact on Childhood Obesity: A Systematic Review
Bibliographic record
Abstract
INTRODUCTION: This systematic review aimed to study the effect of a cooking intervention on obesity among children and adolescents aged < 18 years. METHODS: Articles that studied the effect of cooking intervention with at least 4 sessions among children and adolescents on obesity (from January, 2000 to December, 2021) were included for analysis. Of the 500 articles identified through PubMed and ScienceDirect database, 9 studies qualified to be included in this review. RESULTS: One-third of the studies found a positive effect of a cooking intervention on obesity among children and adolescents. School-based studies conducted among elementary school students were promising. Centers for Disease Control and Prevention body mass index percentile was the most common tool used to identify children and adolescents with overweight and obesity. The majority of the studies had a strong methodology. DISCUSSION: All studies showed improvement in diet-related factors. Active participation of parents is crucial in making childhood interventions successful. It is difficult to delineate the effect of cooking alone on obesity as almost all studies had multicomponent interventions. IMPLICATIONS FOR RESEARCH AND PRACTICE: These diverse results highlight the need for longitudinal studies in natural settings to comprehend the effect of long-term cooking on obesity in children and adolescents.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".