Examining the relationship between obesity and math performance among Canadian school children: A prospective analysis
Bibliographic record
Abstract
OBJECTIVES: To determine whether obesity during the pre-school to primary school years was related to math performance, independently of other factors, in a large representative sample of Canadian children. Our main hypothesis was that obese children would obtain lower overall math scores than their non-obese peers. METHODS: Participants of the National Longitudinal Survey of Children and Youth comprised the sample for our analysis (n = 4 664). Obesity was based on mother-reported height and weight and IOTF age- and sex-specific body mass index cut-offs, and was assessed when the cohort was aged 2-5 years and 8-11 years. Children were classified as 'never obese', 'grew out of obesity', 'developed obesity' or 'always obese', depending on their obesity status at these two time points. The outcome was performance on a standardized math test that was administered when the cohort was aged 8-11 years. Statistical analysis was conducted using multivariate linear regression methods. RESULTS: Children who 'grew out of obesity' scored on average 11 points higher on the math test (p<0.0001) than children who were 'never obese.' Children who were 'always obese' and those that 'developed obesity' performed no differently on the math test than children who were 'never obese'. No interactions between sex and obesity status were found. CONCLUSIONS: Childhood obesity in this study did not lead to poor math performance. Being obese in the pre-school years and normal weight in primary school, however, was associated with improved math performance. This finding points to a potential nutritional window for early child development.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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".