Multiple‐micronutrient supplementation: Evidence from large‐scale prenatal programmes on coverage, compliance and impact
Bibliographic record
Abstract
Micronutrient deficiencies during pregnancy pose important challenges for public-health, given the potential adverse outcomes not only during pregnancy but across the life-course. Provision of iron-folic acid (IFA) supplements is the strategy most commonly practiced and recommended globally. How to successfully implement IFA and multiple micronutrient supplementation interventions among pregnant women and to achieve sustainable/permanent solutions to prenatal micronutrient deficiencies remain unresolved issues in many countries. This paper aims to analyse available experiences of prenatal IFA and multiple micronutrient interventions to distil learning for their effective planning and large-scale implementation. Relevant articles and programme-documentation were comprehensively identified from electronic databases, websites of major-agencies and through hand-searching of relevant documents. Retrieved documents were screened and potentially relevant reports were critically examined by the authors with the aim of identifying a set of case studies reflecting regional variation, a mix of implementation successes and failures, and a mix of programmes and large-scale experimental studies. Information on implementation, coverage, compliance, and impact was extracted from reports of large-scale interventions in Central America, Southeast Asia, South Asia, and Sub-Saharan Africa. The WHO/CDC Logic-Model for Micronutrient Interventions in Public Health was used as an organizing framework for analysing and presenting the evidence. Our findings suggest that to successfully implement supplementation interventions and achieve sustainable-permanent solutions efforts must focus on factors and processes related to quality, cost-effectiveness, coverage, utilization, demand, outcomes, impacts, and sustainability of programmes including strategic analysis, management, collaborations to pilot a project, and careful monitoring, midcourse corrections, supervision and logistical-support to gradually scaling it up.
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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 | 0.030 | 0.155 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".