Delivery Strategies Supporting School-Age Child Health: A Systematic Review
Bibliographic record
Abstract
CONTEXT: School-aged children (SAC; 5-9 years) remain understudied in global efforts to examine intervention effectiveness and scale up evidence-based interventions. OBJECTIVE: This review summarizes the available evidence describing the effectiveness of key strategies to deliver school-age interventions. DATA SOURCES: We searched Medline, PsycINFO, Campbell Collaboration, and The Cochrane Library during November 2020. STUDY SELECTION: Systematic reviews and meta-analyses that: target SAC, examine effective delivery of well-established interventions, focus on low- and middle-income countries (LMICs), were published after 2010, and focus on generalizable, rather than special, populations. DATA EXTRACTION: Two reviewers conducted title and abstract screening, full-text screening, data extraction, and quality assessments. RESULTS: Sixty reviews met the selection criteria, with 35 containing evidence from LMICs. The outcomes assessed and the reported effectiveness of interventions varied within and across delivery strategies. Overall, community, school, and financial strategies improved several child health outcomes. The greatest evidence was found for the use of community-based interventions to improve infectious disease outcomes, such as malaria control and prevention. School-based interventions improved child development and infectious disease-related outcomes. Financial strategies improved school enrollment, food security, and dietary diversity. LIMITATIONS: Relatively few LMIC studies examined facility, digital, and self-management strategies. Additionally, we found considerable heterogeneity within and across delivery strategies and review authors reported methodological limitations within the studies. CONCLUSIONS: Despite limited research, available information suggests community-based strategies can be effective for the introduction of a range of interventions to support healthy growth and development in SAC. These also have the potential to reduce disparities and reach at-risk and marginalized populations.
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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 | 0.010 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".