Availability of emergency neonatal care in eight districts of Karnataka state, southern India: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Emergency Neonatal Care (EmNC) is an important service for the health and survival of newborns. The objective of our study was to assess the availability of emergency neonatal care services in the north-eastern region of Karnataka state in India. METHODS: We undertook a cross-sectional epidemiologic study in the year 2010. We assessed the provision of eight life-saving 'signal functions' (Comprehensive EmNC) or at least five 'signal functions' (Basic EmNC) by self-reporting through a structured questionnaire, coupled with verification by direct observation for presence of drugs and equipment in the prior three months. The assessment was undertaken in 443 government and 422 private healthcare facilities of eight districts of Karnataka. RESULTS: There was an average of 3.6 EmNC facilities available per 500,000 population for the entire region. Only three out of eight districts and 10 of 42 sub-districts in the region had the recommended [greater than or equal to 5] EmNC facilities per 500,000. Further, over 95 % of CEmNC facilities and 88 % of BEmNC facilities were within the private sector. About 80 % of government hospitals at district and sub-district levels did not have EmNC capability. CONCLUSIONS: This study demonstrates the feasibility of using a simple assessment tool to measure health facility availability of life-saving services for newborn care. EmNC availability was seen to be suboptimal at the regional, district and sub-district levels within the northern part of Karnataka state. There is a need to improve availability of emergency newborn care in health facilities, with special emphasis on equity at population level.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".