Barriers to Exclusive Breastfeeding in Mothers Belonging to Low, Middle, and High Socio-Economic Families in Pakistan
Bibliographic record
Abstract
Introduction: Exclusive breastfeeding (EBF) for the first six months of life improves health and increases infant survival. In Asia, Pakistan ranks second in child mortality. Socioeconomic status (SES) is an important predictor of healthy behaviors. The study aims to understand the barriers to exclusive breastfeeding faced by mothers from different socioeconomic classes in Pakistan. Method: The cross-sectional study was done in the Pediatric outpatient department (OPD) of Shifa International hospital and community health center from November 2020 to 1 March 2021. Mothers and their children 1 to 3 years of age were included in the study. Results: A total of 175 mothers and their children were included. EBF for the first six months was done by 120 (68.6%). However, by one year of age, only 99 (56%) continued breastfeeding. Out of these 175 mothers, 79 (45%) were from the lower class, 69 (39.4%) were from the middle class, and 27 (15.4%) were from upper SES. Among the lower, middle, and high socioeconomic classes frequency of EBF practiced by mothers was 51, 54, and 15 (p 0.03), respectively. Pre-lacteals were given to 102 (58.2%). This practice was more frequent in lower SES families (0.03). The majority of working women belonged to upper SES (0.04). Mothers in the upper class are least likely to EBF their children (OR 0.5 CI 0.2-1.3). The adjusted odds ratio for EBF is highest for lower SES mothers, followed by the middle class. Conclusion: Support from family, place and mode of delivery, working mothers, and high socioeconomic class are important factors affecting exclusive breastfeeding in Pakistan.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".