Early predictors of short duration of exclusive breastfeeding among Havana women
Bibliographic record
Abstract
Problem In Cuba, only 40.9% of infants under the age of six months are exclusively breastfed with the average duration of exclusivity being only 2.4 months. Evidence to guide the development of breastfeeding interventions among Cuban women to achieve exclusive breastfeeding to six months is limited. The objective was to identify early predictors for discontinuation of exclusive breastfeeding before six months among Cuban women. Methods In a cohort study, 273 maternal-infant pairs were recruited immediately following childbirth at a public hospital in Havana, Cuba and followed up to six months postpartum. A univariate and multivariate strategy was used to identify early predictors of the discontinuation of exclusive breastfeeding before six months. Results While all women were exclusively breastfeeding at hospital discharge, only 20.5% continued to six months postpartum. The average duration of exclusive breastfeeding was 3.13 months (SD ± 2.14 months). Factors associated with the early discontinuation of exclusive breastfeeding were: (1) breastfeeding not initiated within the first hour of birth, (2) infant birthweight <3.3 kgs, (3) pacifier use, (4) maternal dissatisfaction with infant growth, (5) maternal mental health problems at one month and (6) low breastfeeding self-efficacy at birth and one month. In the multivariate analysis, only maternal dissatisfaction with infant growth progress at one month and birth weight <3.3 kg predicted the early discontinuation of exclusive breastfeeding. Conclusion Women who gave birth to an infant with a lower birth weight and were dissatisfied with their infant's growth trajectory were high risk to prematurely discontinue exclusive breastfeeding before six months.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".