Addressing Quality of Care in Pediatric Units using a Digital Tool: Implementation Experience from 18 SNCU of India
Bibliographic record
Abstract
Lack of quality care is associated with newborn mortality and stillbirth. India launched the Special newborn care unit (SNCU) Quality of Care Index (SQCI) for measuring quality indicators in SNCU. The USAID Vriddhi project provided support to the use of SQCI in 19 SNCU across aspirational districts of Jharkhand, Uttarakhand, Himachal Pradesh, Punjab and Haryana. The objective was to provide holistic support to quality care processes by generating analyzed quarterly reports for action with the goal toward sustainability by capacitating SNCU personnel and program officers to use SQCI, over a 1period from April 2019 to June 2020. The composite index has seven indicators and converts them into indices, each having a range from 0.1 to 1, to measure performance of SNCU.7 of the 18 SNCU improved their composite scores from the first to the last quarter. Rational use of antibiotics showed improvement in 12 SNCU. Survival in newborns >2500 g and <2500, low birth weight admission and optimal bed utilization had the most variations between and within facilities. Based on quarterly data analysis, all facilities introduced KMC, 10 facilities improved equipment and drug supply, 9 facilities launched in-house capacity building to improve asphyxia management. The SQCI implementation helped to show a process of using SQCI data for identifying bottlenecks and addressing quality concerns. The project has transitioned to complete responsibility of SQCI usage by the district and facility teams. Use of an existing mechanism of quality monitoring without any major external support makes the SQCI usable and doable.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".