Nutrition education and counselling by frontline health and nutrition care workers is associated with improved iron adherence among pregnant women: evidence from the National Family Health Surveys, 2015-21
Bibliographic record
Abstract
Background: Anaemia is a global public health problem affecting developing and developed countries with major consequences for human health and social and economic development. Methods: This paper attempted to understand the determinants of iron adherence among women of reproductive age during their last pregnancy in India and states/union territories, the iron deficiency being associated with anaemia, using multiple logistic regression analyses of data from the fourth (2015-16) and fifth (2019-21) rounds of the India National Family Health Survey. The individual unit-level data were analysed using Stata 17 software. Results: After adjusting for standard covariates, it was found that those who reported receiving counselling by a frontline health worker and/or antenatal care visits were much more likely to consume at least 90 days of iron-containing supplements. Results shows that for NFHS-4 women who had received counselling from a frontline health and nutrition care worker were 1.489 times (95% CI: 1.429-1.553) to adhere to at least 90 days of iron tablets or syrup after adjusting for potential confounders and for NFHS-5 women who had received counselling from a frontline worker were 1.162 times (95% CI :1.124-1.200) more likely to adhere to IFA supplements or syrup for at least 90 days. Conclusions: Based on insights from these analyses, it appears that counselling seems to be effective in making women aware of the benefits of IFA, thus increasing the likelihood of intake of iron-containing supplements. Counselling seems to be an important predictor of adherence to iron intake in India.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".