Research priorities for nutrition of school-aged children and adolescents in low- and middle-income countries
Bibliographic record
Abstract
PURPOSE: A lack of data, intervention studies, policies, and targets for nutrition in school-age children (SAC) and adolescents (5-19 years) is hampering progress towards tackling malnutrition. To stimulate and guide further research, this study generated a list of research priorities. METHODS: Using the Child Health and Nutrition Research Initiative (CHNRI) method, a list of 48 research questions was compiled and questions were scored against defined criteria using a stakeholder survey. Questions covered all forms of malnutrition, including micronutrient deficiencies, thinness, stunting, overweight/obesity, and suboptimal dietary quality. The context was defined as research focused on SAC and adolescents, 5 to 19 years old, in low-and middle-income countries, that could achieve measurable results in reducing the prevalence of malnutrition in the next 10 years. RESULTS: Between 85 and 101 stakeholders responded per question. Respondents covered a broad geographical distribution across 38 countries, with the largest proportion focusing on work in East and Southern Africa. Of the research questions ranked in the top ten, half focused on delivery strategies for reaching adolescents and half on improving existing interventions. There were few differences in the ranked order of questions between age groups but those related to in-school children and adolescents had higher expert agreement than those for out-of-school adolescents. The top ranked research question focused on tailoring antenatal and postnatal care for pregnant adolescent girls. CONCLUSION: Nutrition programmes should incorporate implementation research to inform delivery of effective interventions to this age group, starting in schools. Academic research on the development and tailoring of existing nutrition interventions is also needed; specifically, on how to package multisectoral programmes and how to better reach vulnerable and underserved sub- groups, including those out of school.
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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 | 0.030 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| 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".