PROTOCOL: The effectiveness of community, financial, and technology platforms for delivering nutrition‐specific interventions in low‐ and middle‐income countries: A systematic review
Bibliographic record
Abstract
BACKGROUNDAbout half of global under-5 child mortality, or about 3 million deaths, are linked to poor nutrition (UN Inter-agency Group for Child Mortality, 2017; UNICEF, 2018).The effects of compromised nutrition at an early age are evident throughout the life course, with physical and cognitive impairments affecting health, learning, and economic potential (Martins, Toledo Florêncio, & Grillo, 2011).Good nutrition is also important beyond the childhood years, with adolescent girls being especially vulnerable to undernutrition because of their higher nutritional requirements, particularly those who might become pregnant.Therefore, a focus on adolescent girls' nutrition is important to ensure adequate prepregnancy nutrition for maternal, fetal, and infant health.Evidence suggests under-5 child mortality can be reduced by 15% with 90% coverage of 10 evidence-based nutrition interventions (Bhutta, Das, & Rizvi, 2013).However, despite evidence of efficacy generated from controlled settings, the potential impacts of what are considered "proven" nutrition interventions are often not realized in real-world environments due to ineffective delivery channels for achieving high and equitable coverage.A review by Ramakrishnan et al. (2014) noted that while prenatal proteinenergy and iron folic acid supplementation have been shown to reduce low birth weight by 20-30% in trial settings, variable implementation has led to uncertain effectiveness.Menon et al. (2014) also acknowledge evidence supporting effective delivery platforms for nutrition-specific interventions remains limited.Of particular concern are gaps in how to successfully reach adolescents with evidence-based nutrition interventions in low-and middle-income countries (LMIC) (Bhutta, Lassi, & Bergeron, 2017; Salam, Hooda and Das, 2016).Our review considers delivery platforms that can improve coverage of nutrition-specific interventions at all stages of the life course from preconception to pregnancy, infancy, childhood, and adolescence.The review is part of a series of concurrent reviews to produce up-to-date evidence on preventive and curative nutrition interventions across the lifecycle. | Description of the conditionThe review will consider platforms for interventions to address a variety of nutrition-specific conditions.We will examine the effects of using community, financial, and technology platforms for delivering evidence-based nutrition-specific interventions to improve nutrition behaviors and outcomes for women, children, and adolescents in LMICs.For the purpose of our review, a "platform" is defined as a modality through which a service is made available to target beneficiaries.
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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 | 0.046 | 0.067 |
| Meta-epidemiology (narrow) | 0.007 | 0.007 |
| Meta-epidemiology (broad) | 0.024 | 0.020 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.128 | 0.014 |
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".