Essential newborn care practices in selected public health facilities using observation of 2603 normal deliveries in Uttar Pradesh, India
Bibliographic record
Abstract
INTRODUCTION: Essential newborn care (ENBC) practices are recommended for all births to improve neonatal survival. This paper aims to understand the facility-level variations and factors associated with the essential newborn care practices by providers in higher-level public health facilities in 25 high priority districts (HPDs) of Uttar Pradesh (UP). METHODS: We used observational cross-sectional quantitative data from 48 selected public health facilities (23 district hospitals (DH) and 25 community health centres (CHC)-first referral units (FRU)) implemented in 25 HPDs of UP from February 2020 to May 2021. We defined ENBC practice as both cord care and initiation of breastfeeding within 1 hour of birth were practiced in normal deliveries by the staff nurse. Descriptive analysis was done based on data from 2603 newborns attended by 318 providers. A stratified analysis was done by DH and CHC-FRU. RESULTS: Overall, essential newborn care was practiced among 26.1% of the newborns (26.2% in DH and 35.0% in CHC-FRU). The ENBC practice varied across facilities from 3.0% to 64.1% in DH and from 0% to 91.0% in CHC-FRU. The ENBC practice was about 2.3 times higher in facilities with a high level of skill and knowledge of the providers (39.0%) compared with the facilities with a low level of skill and knowledge (16.9%). Similar patterns of association between providers' skills and knowledge of ENBC practices were observed in DH and CHC-FRU. CONCLUSION: Skill and knowledge on ENBC components are significantly associated with the clinical practices of providers, with a high level of variability across facilities. This suggests a focused facility-based assessment and enhancement of the clinical competencies of the providers to improve the quality of care in public health facilities in UP.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".