Micronutrient deficiency, dietary diversity, and sociodemographic and lifestyle determinants of dietary diversity among pregnant slum-dwelling women in Pune, India
Bibliographic record
Abstract
BACKGROUND: Increasing dietary diversity is a sustainable solution to combat micronutrient deficiencies. Given the large slum population in urban India, double burden of malnutrition, nutritional transition among slum-dwellers, and limited studies focusing on dietary intake and diversity among pregnant slum-dwellers, this study aimed to 1) describe macro- and micronutrient intakes and compare them with guidelines, 2) describe dietary diversity and intake of unhealthy foods and, 3) investigate the sociodemographic and lifestyle determinants of adequate dietary diversity among pregnant slum-dwellers in Pune, Maharashtra, India. METHODS: This study presents cross-sectional data of 454 pregnant slum-dwelling women completing mid-pregnancy visit collected from a larger cohort study. Sociodemographic and lifestyle data were collected at baseline (< 12 weeks gestation). Dietary data (24-h dietary recall) were collected in mid-pregnancy (23 ± 2 weeks). Nutrient intakes were compared with the Estimated Average Requirements (EAR) for pregnant Indian women. Dietary diversity score (DDS, range 0-10) and unhealthy food (sweet snacks, sweet beverages, fried and salty food) group score (range 0-3) were calculated as per FAO guidelines. Multivariate logistic regression was conducted to examine determinants of adequate dietary diversity (DDS ≥ 5). RESULTS: ) total energy and protein intakes were 1771 (1456, 2185) kcal/d and 44.7 (34.7, 55.0) g/d, respectively. Total energy and protein were consumed as per EAR by 37% and 54% of women, respectively. Forty percent of women exceeded the recommended energy intake from carbohydrates. Diets of slum-dwelling women were lacking in multiple micronutrients (especially iron, zinc, riboflavin, thiamine, folate). The mean DDS was 4.2 ± 1.2 and 36.5% of the women had DDS ≥ 5. All women consumed mainly cereal-based starchy staples; 80% consumed pulses and legumes, and 60% consumed other vegetables. Fifty-nine percent of women consumed ≥ 2 unhealthy food groups. Higher educational and occupational status of the primary earning members of the family and lower parity were determinants of adequate dietary diversity. CONCLUSION: The diets of pregnant slum-dwelling women were lacking in numerous micronutrients. Dietary counselling programs need to be tailored to the socioeconomic backgrounds of pregnant slum-dwelling women and involve their family members to improve reach and effectiveness.
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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 | 0.000 |
| 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".