Making the Health System Work for Nutrition (P10-139-19)
Bibliographic record
Abstract
The many factors underlying malnutrition highlight the need for nutrition strategies that are comprehensive and multi-sectoral. Within a multi-sectoral approach, the health system is uniquely placed to deliver ten nutrition-specific interventions, which, if scaled up, could substantially reduce under-5 deaths in high-burden countries (Bhutta et al, 2013). This study aims to clarify the role of key health system components, illuminating opportunities for increased uptake of nutrition-specific interventions and potential bottlenecks and challenges for programs and policies. We reviewed existing nutrition frameworks to develop a comprehensive logic model illustrating the causal pathways by which health system components influence household-level determinants of nutrition and individual-level health outcomes. Concurrently, we reviewed literature on health and nutrition interventions that have a proven, quantifiable impact on morbidity and mortality in low- and middle-income countries. We mapped data from the gathered literature onto the logic model, allowing us to identify key causal pathways for the delivery of nutrition-specific interventions, and highlighting areas where evidence is lacking. There exists a large gap in the literature about how health system components influence the ability to delivery appropriate and high-quality nutrition care. Based on the nature of the intervention (i.e., supplement delivery, counselling, SAM/MAM management) several unique delivery pathways were identified and three common themes cut across all interventions: namely, the importance of a motivated and trained health workforce; reliable and efficient supply chains; and generating demand and increased care-seeking for nutrition interventions. Evidence from the broader health systems literature supports the importance of these three themes; however, this remains a research gap in the nutrition literature. Three core health system components - a health workforce, supply chain and demand generation - play a pivotal role in the delivery of nutrition interventions. A better understanding of these components in relation to nutrition, based on evidence, will help to improve the design and implementation of future nutrition programs. Global Affairs Canada, under the project “Real Accountability: Data Analysis for Results”.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.185 | 0.034 |
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".