Predictors of Adherence to Preconception and Prenatal Micronutrient Supplementation in Vietnam
Bibliographic record
Abstract
We examined predictors of adherence to preconception and prenatal micronutrient supplementation, using data from a randomized controlled trial in rural Vietnam. 5011 women of reproductive age were randomized to receive weekly PRECONCEPT supplements containing either: Folic Acid, Iron and Folic Acid (IFA), or Multiple Micronutrients. Women who became pregnant (1744) received daily prenatal IFA (GESTCARE) supplements through delivery. Monitors visited women's homes every 14 d to deliver supplements and record consumption and side effects. 59% and 75% consumed all the tablets that they received for PRECONCEPT and GESTCARE, respectively. In logistic regression analyses, women anemic at baseline (OR, 95% CI=1.25, 1.06 1.47), farmers (OR, 95% CI=1.28, 1.07 1.53), and minority ethnicity (OR, 95% CI=1.50, 1.31 1.71) were more likely to have lower adherence to PRECONCEPT supplements and minority ethnicity with lower adherence to GESTCARE (OR, 95% CI=1.33, 1.01 1.77). There were no differences by type of supplement. Parity, body mass index, side effects, and education did not predict adherence (p>0.05). Adherence to preconception and prenatal supplementation was high among women from a rural province of Vietnam. Lower adherence in vulnerable groups suggests a need to develop tailored behavior change interventions to accompany micronutrient supplementation in this population. Funded by the Micronutrient Initiative and the Mathile Institute.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".