Artificial Infant Formula Consumption and Breastfeeding Trends in Ecuador, A Population-Based Analysis from 2007 to 2014
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to analyze trends in infant breastfeeding and artificial infant milk consumption in Ecuador from 2007 to 2014. METHODS: This descriptive observational study includes all the available data collected and adapted from the National Health and Nutrition Survey of Ecuador, ENSANUT, the Ecuadorian National Institute of Census and Statistics, the national report of the International Marketing Services and data from Enfarma EP. Descriptive and inferential statistics were used to determine sociodemographic distribution and temporal trends. RESULTS: In Ecuador 54% of children initiate breastfeeding during the first hour of life, and 43% of children aged five months are breastfed exclusively. 76% of children under one month of age and 60% of children under six months consume artificial infant formula. Over the last 8 years infant formula consumption has tripled in Ecuador reaching 59.6 million units sold at a cost of $530,100,000 USD from 2007 to 2014. CONCLUSIONS: Breastfeeding practices in Ecuador are not complying with WHO recommendations and infant milk formulas consumption has risen significantly since 2007, despite active campaigns by the public health sector to educate women as to the benefits of breastfeeding.
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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 | 0.002 |
| 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".