Determinants of Early Initiation of Breastfeeding among Mothers of Children Aged Less Than 24 Months in Northwestern Romania
Bibliographic record
Abstract
Early initiation of breastfeeding (EIBF), defined as putting newborns to the breast within 1 h of birth, may have important benefits for both infant and mother. The aim of this study was to assess EIBF practices and its determinants in northwestern Romania. This cross-sectional study was conducted from March to June 2019, based on a sample of 1399 mothers of children aged less than 24 months. The sample was recruited from the community, from 29 cities and 41 communes distributed across the six counties of the northwestern region of Romania. Mothers responded by face-to-face interviews to a structured questionnaire. Multivariate logistic regression was used to identify factors independently associated with EIBF. Only 24.3% of the mothers initiated breastfeeding within 1 h of birth. Delivering at a private hospital (adjusted odds ratio (AOR): 5.17, 95% confidence interval (CI) 3.87, 6.91), vaginal delivery (AOR: 4.39, 95% CI 3.29, 5.88), mother-newborn skin-to-skin contact for 1 h or more (AOR: 55.6, 95% CI 23.0, 134.2), and breastfeeding counseling during antenatal visits (AOR: 1.48, 95% CI 1.12, 1.97) were factors associated with increased likelihood of EIBF. Overall, the practice of EIBF was poor. Targeting modifiable factors associated with EIBF may be used to improve early initiation practice.
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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 | 0.001 |
| 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".