Facilitators and barriers to implementing complex community-based interventions for addressing acute malnutrition in low- and lower-middle income countries: A scoping review
Bibliographic record
Abstract
Background: Community-based nutrition interventions have been established as the standard of care for identifying and treating acute malnutrition among children 6–59 months in low- and lower-middle-income countries. However, limited research has examined the factors that influence the implementation of the community-based component of interventions that address severe acute malnutrition and moderate acute malnutrition among children. Aim: The objective of this review was to identify and describe the facilitators and barriers in implementing complex community-based nutrition interventions to address acute malnutrition among children in low- and lower-middle-income countries. Methods: This review used a systematic search strategy to identify existing peer-reviewed literature from three databases on complex community-based interventions (defined as including active surveillance, treatment, and education in community settings) to address severe acute malnutrition and moderate acute malnutrition in children. Results: In total, 1771 sources were retrieved from peer-reviewed databases, with 38 sources included in the review, covering 26 different interventions. Through an iterative deductive and inductive analysis approach, three main domains (household and interpersonal, sociocultural and geographical; operational and administrative) and eight mechanisms were classified, which were central to the successful implementation of complex community-based interventions to address acute child malnutrition. Conclusion: Overall, this review highlights the importance of addressing contextual and geographical challenges to support participant access and program operations. There is a need to critically examine program design and structure to promote intervention adherence and effectiveness. In addition, there is an opportunity to direct resources towards community health workers to facilitate long-term community trust and engagement.
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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 | 0.034 | 0.107 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".