Nutritional status of Makushi Amerindian children and adolescents of Guyana
Bibliographic record
Abstract
BACKGROUND: Amazonian Indians are in the midst of a rapid cultural transition. The developments affecting Amazonian Indians present an opportunity to address important public health problems through public and private initiatives, but to do so it is imperative to begin with information on the health status of these peoples and the underlying factors affecting it. However, relatively few such data are available for this vast region. AIM: This study describes the nutritional status of Makushi Amerindians of Guyana and considers several variables which might help to explain it. SUBJECTS AND METHODS: Data for 792 Makushi, 0-20 years of age from 11 villages are considered. Outcome variables considered are anthropometric markers of growth and nutritional status; specifically height-for-age, weight-for-height and body-mass index. Predictor variables explored are age, sex, relative isolation, number of siblings, season of birth, diet and morbidity. Fisher's exact test, chi-square, Pearson's correlation and multiple regression were used to assess possible relationships between these variables. RESULTS: Relative to other Amazonian Indians, the Makushi have a lower rate of linear-growth faltering and a higher rate of linear-growth faltering relative to non-Amerindian Guyanese. Males, older cohorts, those living in isolated villages or born in the wet season showed higher rates of growth faltering. CONCLUSION: Makushi nutritional status may be explained by sex, age, relative isolation, family size, season of birth, dietary intake and infectious disease.
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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 | 0.000 |
| 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".