Determinants of Undernutrition among Young Children Living in Soth Nikum District, Siem Reap, Cambodia
Bibliographic record
Abstract
BACKGROUND: Child undernutrition is of public concern in Cambodia. An understanding of factors influencing child nutritional status is essential to design programs that will reduce undernutrition. Using the UNICEF conceptual framework of causes of malnutrition, our research investigates the relationship between nutritional status of children aged 6⁻23 months and its immediate and underlying determinants. METHODS: Baseline data from a cluster-randomized controlled trial aiming to assess the impact of the promotion of optimal feeding practices combined or not with the provision of local foods among 360 children 6⁻23 months of age were used. Anthropometry and biochemical measurements were performed at baseline. Data on each determinant of undernutrition were collected through interviews and direct observations. RESULTS: Our results show that the degree of satisfaction of proteins and zinc requirements as well as the access to improved water sources and sanitation were positively associated with length-for-age, while having a better health status and a higher degree of satisfaction of energy, protein, zinc, and iron requirements were associated to an improved weight-for-length. Only child health status was associated to ferritin. CONCLUSION: Our results reiterate the importance of improving child diet and health status, but also the access to a healthy environment to ensure an optimal nutritional status.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".