Interventions to increase adherence to micronutrient supplementation during pregnancy: a systematic review
Bibliographic record
Abstract
Prenatal micronutrient supplements are cost-effective in reducing nutritional deficiencies and adverse pregnancy and birth outcomes. However, poor adherence remains a potential barrier to the successful implementation of these supplementation programs. This systematic review assessed the effectiveness of interventions designed to increase adherence to prenatal micronutrient supplementation. Following the Cochrane Collaboration Methodology, literature searches were conducted in six electronic databases and gray literature (on July 24, 2020), and abstract screening, data extraction, and risk of bias assessment were conducted independently by two reviewers. We included 22 studies. Interventions that resulted in increased adherence were most of the education-based strategies, consumption monitoring by volunteer health workers or family members, SMS reminders, free provision of supplements, a multicomponent intervention with community mobilization, and a participatory action research intervention. In several studies, increased adherence was accompanied by beneficial effects on pregnancy and birth outcomes. Given the heterogeneity of study designs and methods used to define and measure adherence, a meta-analysis was not appropriate. We identified several potentially effective strategies to improve supplementation adherence, which may need to be adapted to specific contexts when considered for program implementation. However, additional high-quality studies are critically needed to effectively guide policies and programs.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".