Changes over time in early complementary feeding of breastfed infants on the island of Hispaniola.
Bibliographic record
Abstract
OBJECTIVE: To describe and contrast early complementary feeding (ECF) over time in breastfed infants in the Dominican Republic (DR) and Haiti, the two countries that share the island of Hispaniola. METHODS: Secondary data analysis was conducted on cross-sectional data from Demographic and Health Surveys administered at four different time-points in both countries between 1994 and 2013. Extracted samples were composed of breastfed infants < 6 months of age whose caregivers had responded to dietary questions on food consumption in the previous 24 hours. RESULTS: Plain water was the most frequently consumed complementary substance in both countries. However, the prevalence of water consumption increased in the DR over time, whereas in Haiti it decreased. Milk (non-breast) use was also common and followed a similar pattern as water over time in the two countries. Expanded use of water and milk in the DR are the major contributors to its drop in exclusive breastfeeding (EBF) rates over time. Whereas in Haiti, a reduction in a broader array of liquids and semi-solids/solids overtime appears to have contributed to its markedly improved EBF rates. CONCLUSION: Determining contributors to the differential trends in water and milk (non-breast) use between these two countries may identify targets for addressing the persistent gaps in EBF on the island of Hispaniola.
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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 | 0.001 | 0.001 |
| 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".