Newborn care practices in rural Bangladesh
Bibliographic record
Abstract
Background: Neonatal mortality is high in Bangladesh. Most of the neonatal deaths are preventable through simple and cost-effective essential newborn care interventions. Studies to document the determinants of unhealthy newborn care practices are scarce. Objective: The objective of this study is to describe the pattern of neonatal care practices and their determinants in rural Bangladesh. Methodology: This study is based on baseline data of a community-based intervention to assess impact of limited postnatal care services on maternal and neonatal health-seeking behavior. Data from 510 women, who had a live birth at home 1 year prior to survey, of six randomly selected unions of an Upazila (subdistrict) were analyzed. Results: Majority of the respondents were at an age group of 20–34 years. Only 6% had delivery by skilled providers. Immediate drying and wrapping, and giving colostrums to newborns were almost universal. Unhealthy practices, like unclean cord care (42%), delayed initiation of breastfeeding (60%), use of prelacteals (36%), and early bathing (71%) were very common. Muslims were more likely to give early bath (adjusted odds ratio [OR]: 2.01; 95% confidence interval [CI]: 1.13–3.59; P =0.018) and delay in initiating breastfeeding (adjusted OR: 1.45; 95% CI: 1.18–1.78; P <0.001) to newborns. Practice of giving prelacteals was associated with teenage mothers (adjusted OR: 2.26; 95% CI: 1.19–4.28; P =0.013) and women’s lack of education (adjusted OR: 2.64; 95% CI: 1.46–4.77; P =0.001). Conclusion: Unhealthy neonatal care practices are widespread in rural Bangladesh. Continued education to the community and home delivery attendants on essential newborn care could benefit newborn survival in Bangladesh. Keywords: newborn care, cord care, bathing, breastfeeding, prelacteals, determinants, Bangladesh
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".