Barriers to and Facilitators of the Implementation of a Micronutrient Powder Program for Children: A Systematic Review Based on the Consolidated Framework for Implementation Research
Bibliographic record
Abstract
BACKGROUND: As one of the most cost-effective investments for improving child nutrition, micronutrient powder (MNP) has been widely used in many countries to underpin the Sustainable Development Goals, yet challenges remain regarding its implementation on a large scale. However, few studies have explored the factors that facilitate or impede the implementation process using implementation science theories and frameworks. To address this gap, we adopted the Consolidated Framework of Implementation Research (CFIR) and conducted a systematic review of studies on the implementation barriers to and facilitators of MNP interventions. METHOD: Five publication databases, including EMBASE, Medline, PubMed, Web of Science, and Scopus, were searched for studies on the influencing factors of MNP interventions. Based on the CFIR framework, the facilitators and barriers for the MNP program implementation reported in the included studies were extracted and synthesized by five domains: intervention characteristics, outer setting, inner setting, individual characteristics, and process. RESULTS: A total of 50 articles were eligible for synthesis. The majority of the studies were conducted in lower-middle-income countries (52%) through the free delivery model (78%). The inner setting construct was the most prominently reported factor influencing implementation, specifically including available resources (e.g., irregular or insufficient MNP supply), structural characteristics (e.g., public-driven community-based approach), and access to information and knowledge (e.g., lack of training for primary-level workers). The facilitators of the engagement of private sectors, external guidelines, and regular program monitoring were also highlighted. On the contrary, monotonous tastes and occasional side effects impede intervention implementation. Additionally, we found that the inner setting had an interrelation with other contributing factors in the MNP program implementation. CONCLUSION: Our results suggest that MNP program implementation was prominently influenced by the available resources, organizational structure, and knowledge of both providers and users. Mobilizing local MNP suppliers, engaging public-driven free models in conjunction with market-based channels, and strengthening the training for primary-level health workers could facilitate MNP interventions.
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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 | 0.052 | 0.137 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.017 |
| Bibliometrics | 0.021 | 0.020 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".