National nutrition strategies that focus on maternal, infant, and young child nutrition in Southeast Asia do not consistently align with regional and international recommendations
Bibliographic record
Abstract
We examined the consistency of national nutrition strategies and action plans (NNS) focusing on maternal, infant, and young child nutrition in Southeast Asia with regional and international recommendations. Between July and December 2017, we identified and extracted information on context, objectives, interventions, indicators, strategies, and coordination mechanisms from the most recent NNS in nine Southeast Asian countries. All NNS described context, objectives, and the following interventions: antenatal care, micronutrient supplementation during pregnancy, breastfeeding promotion, improved complementary feeding, nutrition in emergencies, and food fortification or dietary diversity. Micronutrient supplementation for young children was included in eight NNS; breastfeeding promotion during pregnancy and support at birth in seven; and school feeding, deworming, and treatment of severe acute malnutrition in six. All NNS contained programme monitoring and evaluation plans with measurable indicators and targets. Not all NNS covered wasting, exclusive breastfeeding, low birthweight, and childhood overweight. Strategies for achieving NNS goals and objectives were health system strengthening (nine), social and behaviour change communication (nine), targeting vulnerable groups (eight), and social or community mobilization (four). All addressed involvement, roles and responsibilities, and collaboration mechanisms among sectors and stakeholders. There was a delay in releasing NNS in Indonesia, Myanmar, and the Philippines. In conclusion, although Southeast Asian NNS have similarities in structure and contents, some interventions and indicators vary by country and do not consistently align with regional and international recommendations. A database with regularly updated information on NNS components would facilitate cross-checking completeness within a country, comparison across countries, and knowledge sharing and learning.
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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 | 0.025 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".