Infant and young child feeding practices and stunting in two highland provinces in Ecuador
Bibliographic record
Abstract
The first two years of life are critical for growth and development. Little is known about infant and young child feeding (IYCF) practices in the Ecuadorian highlands and how they contribute to stunting. With the objective of understanding nutritional status and the influencing factors to design an intervention, we assessed the nutritional status of 293 infants and children between 0 and 24 months of age, living in 14 communities in the provinces of Tungurahua and Chimborazo using a cross-sectional study design. We used the WHO IYCF indicators to assess feeding practices; estimated dietary intake with 24-h recalls; and identified nutritious local foods by food frequency questionnaires. Multiple regression modelling was performed to identify correlates of nutritional status. Stunting was found in 56.2% of children. Mean protein, vitamin A and vitamin C intakes were above recommendations for all ages. Only infants 6.0 to 8.9 months of age and non-breastfed children 12-23.9 months of age consumed energy intakes below recommendations. Younger age groups had below recommended intakes for iron and calcium. While mean complementary food densities met recommendations for protein, vitamin A, vitamin C and energy, those for zinc, iron and calcium were lower than recommended. Older age, respiratory infections and being male were predictors of lower HAZ, whereas early initiation of breastfeeding, higher socioeconomic status, consumption of iron-rich foods and higher dietary protein density were protective. Interventions that promote and support optimal breastfeeding practices and enable increased consumption of nutritious local foods have potential to contribute to reducing stunting in this vulnerable population. © 2016 John Wiley & Sons Ltd.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".