Influence of Integrated Community- and Facility-based Interventions on Select Maternal and Neonatal Outcomes in Northern Karnataka, India: Lessons for Implementation and Measurement
Bibliographic record
Abstract
BACKGROUND: Sukshema project provided technical assistance to National Health Mission of government of Karnataka to improve maternal, newborn and child health (MNCH) outcomes in eight districts of Karnataka between 2009 and 2015. The project designed tools, processes and provided mentoring to frontline workers, community structures, and facilities to improve demand generation and quality of MNCH services. OBJECTIVES: To assess over time changes in selected MNCH care indicators among women who had delivered in the past 2 months in Bagalkot and Koppal districts. METHODS: An innovative strategy was designed to collect routine monitoring data, namely community behavior tracking survey using mobile technology. The catchment area of an Accredited Social Health Activist (ASHA) was the primary sampling unit, and in each district 200, ASHA areas were selected. Women from these selected ASHA areas were interviewed and information collected on various MNCH care outcomes. Multivariate logistic regression was used to assess changes in selected MNCH care indicators. RESULTS: Gradual increase was noticed in institutional delivery, hospital stay for 48 or more hours, initiation of breastfeeding within 1 hour and continuum of MNCH care. Forty-eight hours stay and initiation of breastfeeding improved marginally possibly due to health systems and cultural norms. CONCLUSIONS: Results indicated that the interventions were successful in changing the critical MNCH care indicators and hence have potential for replication in similar high priority district settings.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".