Indirect health sector actions and supportive strategies to prevent malnutrition
Bibliographic record
Abstract
PURPOSE OF REVIEW: Malnutrition is a pervasive problem that causes negative acute, long-term, and intergenerational consequences. As we have begun to move from efficacy to effectiveness trials of nutrition interventions, and further still to more holistic case study approaches to understanding how and why nutrition outcomes change over time, it has become clear that more emphasis on the 'nutrition-sensitive' interventions is required. RECENT FINDINGS: In this article, we propose recategorizing the nutrition-specific and sensitive terminology into a new framework that includes direct and indirect health sector actions and supportive strategies that exist outside the health sector; an adjustment that will improve sector-specific planning and accountability. We outline indirect health sector nutrition interventions, with a focus on family planning and the evidence to support its positive link with nutrition outcomes. In addition, we discuss supportive strategies for nutrition, with emphasis on agriculture and food security, water, sanitation, and hygiene, and poverty alleviation and highlight some of the recent evidence that has contributed to these fields. SUMMARY: Indirect health sector nutrition interventions and supportive strategies for nutrition will be critical, alongside direct health sector nutrition interventions, to reach global targets. Investments should be made both inside and outside the health sector.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".