Factors associated with body mass index among slum dwelling women in India: an analysis of the 2005–2006 Indian National Family Health Survey
Bibliographic record
Abstract
BACKGROUND: Urbanization is increasing around the world, and in India, this trend has translated into an increase in the size of slum dwellings whose environments are suspected of being associated with poor health outcomes, particularly those relating to women's nutritional status. With this study, we sought to determine the factors associated with Indian women's body mass index (BMI) in slum environments, with special attention paid to women with tribal status. METHODS: A multiple linear regression analysis was performed on data from the Indian National Family Health Survey (2005-2006), modeling demographic and behavioral factors suspected of being associated with BMI, with additional focus on the measures of social class, specifically caste and tribal status. RESULTS: Increasing BMI is significantly and positively associated with frequency of watching television, having diabetes, age, wealth index, and residency status in the areas of New Delhi, Andhra Pradesh, or Tamil Nadu. CONCLUSION: Although belonging to a scheduled tribe was not associated with changes in BMI, unadjusted rates suggest that tribal status may be worthy of deeper investigation. Among slum dwellers, there is a double burden of undernutrition and overnutrition. Therefore, a diverse set of interventions may be required to improve the health outcomes of these women.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".