Readiness to provide child health services in rural Uttar Pradesh, India: mapping, monitoring and ongoing supportive supervision
Bibliographic record
Abstract
BACKGROUND: In 2018, 875 000 under-five children died in India with children from poor families and rural communities disproportionately affected. Community health centres are positioned to improve access to quality child health services but capacity is often low and the systems for improvements are weak. METHODS: Secondary analysis of child health program data from the Uttar Pradesh Technical Support Unit was used to delineate how program activities were temporally related to public facility readiness to provide child health services including inpatient admissions. Fifteen community health centres were mapped regarding capacity to provide child health services in July 2015. Mapped domains included human resources and training, infrastructure, equipment, drugs/supplies and child health services. Results were disseminated to district health managers. Six months following dissemination, Clinical Support Officers began regular supportive supervision and gaps were discussed monthly with health managers. Senior pediatric residents mentored medical officers over a three-month period. Improvements were assessed using a composite score of facility readiness for child health services in July 2016. Usage of outpatient and inpatient services by under-five children was also assessed. RESULTS: The median essential composition score increased from 0.59 to 0.78 between July 2015 and July 2016 (maximum score of 1) and the median desirable composite increased from 0.44 to 0.58. The components contributing most to the change were equipment, drugs and supplies and service provision. Scores for trained human resources and infrastructure did not change between assessments. The number of facilities providing some admission services for sick children increased from 1 in July 2015 to 9 in October 2016. CONCLUSIONS: Facility readiness for the provision of child health services in Uttar Pradesh was improved with relatively low inputs and targeted assessment. However, these improvements were only translated into admissions for sick children when clinical mentoring was included in the support provided to 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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".