Nobody left behind? Equity and the drivers of stunting reduction in Vietnamese ethnic minority populations
Bibliographic record
Abstract
Abstract Vietnam has successfully reduced population stunting, but ethnic minority groups are being systematically left behind, limiting progress on national reductions. This mixed methods study aims to understand how policy drivers of stunting reduction differ between ethnic majority and minority communities. We used decomposition analysis to explain key determinants of stunting change between 2000 and 2010; and framework analysis to qualitatively assess changes in policy, actors and narratives that have underpinned these over decades. Our analysis shows that stunting reductions are associated with increased household wealth (accounting for 61% of change), improved access to specific health services (16%), and changes in level of maternal education (12%). Despite multiple actors involved in change and a large set of policies designed to address inequities, many among Vietnam’s defined ethnic minority groups are not finding themselves able to effectively engage with central government plans for their communities, and central policies often do not consider their preferences or limitations. This in turn impacts the nutrition of minority groups through the determinants above. Vietnam has achieved the easier portion of stunting reduction through national economic growth and sustained commitment to socially-oriented policy. In order to tackle the remaining pockets of high malnutrition, more attention, thought and funding will need to focus on marginalised ethnic minority communities. The current national development discourse aims to incorporate minorities into mainstream majority systems. This paper argues that policy should rather take into account their particular needs and preferences to address and overcome the identified determinants of malnutrition.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".