The prevalence of underweight, overweight and obesity in two groups of children (St. Kitt and Trinidad), using two methods of classification
Bibliographic record
Abstract
Background: This study compares prevalence estimates of underweight and excess weight among two groups of children from the Caribbean islands of St. Kitts and Trinidad, according to two Body Mass Index reference cut-points.The cut-points are based on growth references generated by the World Health Organization (WHO) and the International Obesity Task Force (IOTF).Methods: Heights and weights were obtained from both cohorts of children from St. Kitts (n=189) and Trinidad (n=463) during the baseline assessment of the 'Improving the nutrition and health of CARICOM populations through sustainable agricultural technologi es that increase food availability and diversity of food choices' (2011).Prevalence of underweight, overweight and obesity was estimated using the WHO (2007) and IOTF growth cut-offs.Results: Irrespective of classification method the prevalence of overweight and obesity were high: St. Kitts-12.3%and 8.0% (WHO); 7.0% and 3.7% (IOTF), Trinidad-14.4% and 12.7% (WHO) and 12.3% and 10.3% (IOTF), respectively.Underweight estimates also varied: St. Kitts-2.1% (WHO); 25.7% (IOTF), Trinidad-5.0%(WHO) and 13.6% (IOTF), respectively.Conclusion: There is a lack of consistency between the two main international growth references in accessing weight status in children and adolescents.The IOTF growth reference produces higher estimates of underweight compared to the WHO and lower rates of overweight and obesity, which were all statistically significant.However there is virtually no stunting in the populations, with underweight children appearing to be as tall as children in other weight categories.Therefore the question of dual burden of underweight and excess weight in the groups from St. Kitts and Trinidad remains unclear.Therefore when interpreting the prevalence estimates of underweight, overweight and obesity for children it is paramount to consider the weight classification used.equipped me with the tools to grow as a researcher.Professor KGD is especially appreciated for her guidance through the research process and her helpful and reassuring words.I would like to acknowledge, Dr. Leroy Phillip, principle investigator of the CARICOM project and my committee, for first of all letting me contribute to the 'Improving the nutrition and health of CARICOM populations through sustainable agricultural technologies that increase food availability and diversity of food choices' project.I would also like to thank him for his insightful
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".