Understanding multifactorial drivers of child stunting reduction in Exemplar countries: a mixed-methods approach
Bibliographic record
Abstract
BACKGROUND: Several countries have notably reduced childhood stunting relative to economic growth over the past 15-20 y. The Exemplars in Stunting Reduction project, or "Exemplars," studies success factors among these countries with a lens toward replicability. OBJECTIVES: This paper details the standardized mixed-methods framework for studying determinants of childhood stunting reduction applied in Exemplars studies. METHODS: An expert technical advisory group (TAG), criteria for identifying Exemplar countries, evidence-based frameworks, mixed methodologies (quantitative, qualitative, policy, literature review), effective research partnerships, case study process and timeline, and data triangulation and corroboration are presented. RESULTS: Experts in health, nutrition, and evaluation methods were selected at the study outset to provide technical support to all phases of research (TAG). Exemplar countries were selected by the TAG, who considered quantitative data (e.g., annual rates of stunting change compared with economic growth, country population size) and qualitative insights (e.g., logistics of country work, political stability). Experienced country research partners were selected and an inception meeting with stakeholder consultations was held to launch research and garner support. Evidence-based conceptual frameworks underpinned all Exemplars research activities. A systematic review of published peer-reviewed and grey literature was undertaken, along with in-depth policy and program analysis of nutrition-specific and -sensitive investments. Both descriptive and advanced quantitative analysis was undertaken (e.g., equity analyses, difference-in-difference regression, Oaxaca-Blinder decomposition). Qualitative data collection using in-depth interviews and focus groups was conducted with national and community stakeholders (i.e., child care workers and mothers) to understand country experiences. The case study process was iterative, and all research outputs were triangulated to develop the stunting reduction narrative for each country. Findings were shared with country experts for weigh-in and corroboration through dissemination events. CONCLUSIONS: Exemplars research uses a mixed-methods framework for studying positive outliers that can be applied across diverse health and development outcomes.
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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 | 0.145 | 0.120 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".