Overweight, obesity and underweight in rural black South African children
Bibliographic record
Abstract
Background: The objective was to estimate the prevalence of overweight, obesity and underweight conditions among rural black children in South Africa. A cross-sectional study was undertaken. The setting was Mankweng and Toronto, both rural settlements in Capricorn district, Limpopo province, South Africa.Participants were 1172 school children (541 boys and 631 girls) aged 10–16 years.Method: The prevalence of overweight, obesity and underweight was examined, using the Centers for Disease Control and Prevention (CDC) body mass index (BMI) cut-off points. Height and body weight were measured using standard techniques. Results were analysed with student t-test statistics, with probability level set at p-value ≤ 0.05.Results: The percentage of children who were at risk of overweight were higher in girls (11%) than boys (9.1%), whereas obesity occurred more among the boys (5.5%), compared with the girls (4.4%). Applying the CDC cut-off points of 5th < percentile to define underweight, 25 (4.6%) and 35 (5.2%) of boys and girls respectively were underweight.Conclusion: Similar to previous studies, this study indicates that overweight and obesity are high among South African children, even in rural settings. The study also demonstrates that underweight is prevalent among the sampled children. This supports the notion of a double burden of disease in developing countries.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".