Rapid increase of overweight and obesity among primary school-aged children in the Caribbean; high initial BMI is the most significant predictor
Bibliographic record
Abstract
To examine predictors of increasing overweight among children in two developing countries. Primary school children (6–10 y at baseline, n = 336) and their caregivers. Longitudinal data were collected in 2012, with follow-up 18 months later. Data on children’s height, weight and dietary intake were collected within 8 primary public schools in Trinidad and 7 schools in St. Kitts. Caregivers’ demographic and anthropometric data were also collected. At baseline, children’s age and sex and caregivers’ BMI, age, and marital status and reported dietary intake were similar across all weight groups. The incidence of overweight and obesity among children was 8.8% and 8.1%, respectively. Dietary intake at baseline was not related to becoming overweight or obese. Similarly there were no differences in reported intake among children who became overweight or obese except that they consumed fewer fruits (0.54±0.92 vs. 0.98±1.66, p = 0.017). Misreporting of energy intake was higher among overweight/obese children as compared to those who were not overweight/obese (27% vs. 17%, p = 0.047). The baseline predictors of increasing BMI (adjusted) of the children were older age, higher baseline BMI z-score and higher height-for-age (HFA) z-score; caregiver BMI, children’s energy intake (with adjustment for misreporting) did not predict changes in children’s BMI. The increasing prevalence of overweight/obesity among children is a serious problem in the Caribbean. Heavier children are at elevated risk of continued rapid increase in their weight status, pointing to the need for early intervention.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".