Nutrition status in adult Chilean population: economic, ethnic and sex inequalities in a post-transitional country
Bibliographic record
Abstract
OBJECTIVE: To assess the relationship between malnutrition, socioeconomic status (SES) and ethnicity in Chilean adult population. DESIGN: Nationally representative survey (ENS) conducted in 2016-2017. Sociodemographic information, weight, height and hemoglobin (Hb) were measured (2003 ENS). Excess weight was defined as BMI ≥25 kg/m2. Undernutrition included underweight (BMI <18·5 kg/m2), short stature (height <1·49 m in women and <1·62 m in men) or anaemia (Hb <12 g/l). Education and household income level were used as indicators of SES; ethnicity was self-reported. We applied linear combinations of estimators to compare the prevalence of excess weight and undernutrition by SES and ethnicity. SETTING: Chile. PARTICIPANTS: In total, 5082 adults ≥20 years (64 % women) and 1739 women ≥20 years for anaemia analyses. RESULTS: Overall, >75 % of women and men had excess weight. Low SES women either by income or education had higher excess weight ((82·0 (77·1, 86·1) v. 65·0 (54·8, 74·1)) by income; (85·3 (80·6, 89·0) v. 68·2 (61·6, 74·1) %) by education) and short stature (20-49 years; 31(17·9, 48·2) v. 5·2 (2·2,11·4) by education); obesity was also more frequent among indigenous women (20-49 years; 55·8 (44·4, 66·6) v. 37·2 (32·7, 42·0) %) than non-indigenous women. In men, excess weight did not significantly differ by SES or ethnicity, but short stature concentrated in low SES (20-49 years; 47·6 (24·6, 71·6) v. 4·5 (2·1, 9·5) by education) and indigenous men (21·5 (11·9, 5·5, 11·9) v. 8·2 (5·5, 11·9)) (P < 0·05 for all). CONCLUSIONS: In Chile, malnutrition is disproportionately concentrated among women of low SES and indigenous origin; these inequalities should be considered when implementing prevention policies.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".