Socioeconomic Disparities in Accessing Early Newborn Care in Pakistan: Secondary Data Analysis of Nationally Representative Sample
Bibliographic record
Abstract
Objective: Pakistan ranks third in newborn mortality. The study aims to examine any socioeconomic disparities in 48-hour newborn care practices in Pakistan using 6 signal functions. Materials and Methods: Using R (version 4.3.1), a secondary analysis of 3936 mothers' Pakistan Demographic and Health Survey 2017-2018 data was performed. Newborn care practices in 48 hours of life were measured using 6 indicators: cord examination, temperature measurement, danger sign counseling, breastfeeding counseling, breastfeeding observation, and weight measurement. The outcome variable was defined as completing at least 2 signal functions. The frequencies of explanatory variables were estimated using descriptive analysis. Multivariate logistic regression was performed between independent variables and at least 2 signal functions. Results: Among mothers practicing the most newborn care, 71.8% were from urban areas, 81.9% were among the richest, 68.9% had institutional deliveries, 71.3% had 4 or more antenatal care (ANC) visits, 81.5% had cesarean sections (C-sections), and 68.1% were attended by skilled birth attendants. After adjusting for covariates, the likelihood of having at least 2 signal functions was 2.46 times greater for C-sections and 1.58 times greater for institutional deliveries, 2.41 times more probable for mothers with over 4 ANC visits, 1.75 times more likely for those with skilled birth attendants, and 1.64 times more common for the richest mothers. Conclusion: Wealth, C-sections, institutional births, skilled birth attendants, and frequent ANC visits were related to higher care levels, indicating the need for targeted measures in vulnerable populations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".