Is the quality of public health facilities always worse compared to private health facilities: Association between birthplace on neonatal deaths in the Indian states
Bibliographic record
Abstract
BACKGROUND: The role of place of delivery on the neonatal health outcomes are very crucial. Although the quality of care is being improved, there is no consensus about who is the better healthcare provider in low and middle-income countries (LMICs), public or private facilities. The aim of this study is to assess the differentials in neonatal mortality by the type of healthcare providers in India and its states. METHODS: We used the data from the fourth wave of the National Family Health Survey 2015-16 (NFHS-4). Information on 259,627 live births to women within the five years preceding the survey was examined. Neonatal mortality rates for state and national levels were calculated using DHS methodology. Multi-variate logistics regression was performed to find the effect of birthplace on neonatal deaths. Propensity score matching (PSM) was used to evaluate the relationship between place of delivery and neonatal deaths to account for the bias attributable to observable covariates. RESULTS: The rise in parity of the women and purchasing power influences the choice of healthcare providers. Increased neonatal mortality was found in private hospital delivery compared to public hospitals in Punjab, Rajasthan, Chhattisgarh, Madhya Pradesh, Bihar, Jharkhand, Odisha, Goa, Maharashtra, Andhra Pradesh and Karnataka states using propensity score matching analysis. However, analysis on the standard of pre-natal and post-natal care indicates that private hospitals generally outperformed public hospitals. CONCLUSIONS: The study observed a significant variation in neonatal mortality among public and private health care systems in India. Findings of the study urges that more attention be paid to the improve care at the place of delivery to improve neonatal health. There is a need of strengthened national health policy and public-private partnerships in order to improve maternal and child health care in both private and public health facilities.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".