Determinants of Exclusive Breastfeeding in South Gujarat Region of India
Bibliographic record
Abstract
OBJECTIVE: To estimate the situation of breastfeeding in Surat among infants and to determine variables associated to major risks for early weaning. DESIGN AND SETTINGS: Mothers coming to the well baby clinic for immunization of infants at Government Medical College and Hospital were interviewed using pretested questionnaire. SUBJECTS: Mothers with their infants who have not completed one year of age. METHODS: In this cross sectional study, 498 mothers were selected for study from May to September, 2008. Survival analysis was the method used to calculate the prevalence and the median duration of breastfeeding. The Chi-square test was performed to compare the proportions; significance level was set at 5%. Odds ratio was used to measure the significance of association, with a 95% confidence interval. Logistic regression analysis was used to identify the risk factors for early weaning. RESULTS: The median length of exclusive breastfeeding was 6 months. Risk factors for early weaning were primiparity (OR = 3.01, 95% CI = 2.01- 4.51), consecutive delivery interval less than 24 months (OR = 1.79, 95% CI = 1.09 - 2.92), maternal age below 20 years (OR = 6.49, 95% CI = 2.69 - 15.61), and paternal occupation as labor (OR = 2.02, 95% CI = 1.36 - 3.00). CONCLUSIONS: Exclusive breastfeeding practices are not in a better situation than at national level. The factors related to early weaning denote a weak breastfeeding support given by maternal and infant health services. KEYWORDS: Exclusive breastfeeding; Weaning; Antenatal care; Postnatal care; Education.
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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 | 0.001 |
| 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".