Examining the role of Accredited Social Health Activists (ASHAs) in improving uptake of essential primary health care services for mothers and children in India.
Bibliographic record
Abstract
Health inequalities continue to impede access to essential primary healthcare for mothers and children in low- and middle-income countries (LMICs). Community Health Workers (CHWs), as culturally adept community members, are important in enhancing primary healthcare delivery. However, the effectiveness of CHW programs and the impact of socio-economic attributes of health-seekers on health-seeking behaviors remain underexplored. This dissertation aims to address these gaps, focusing on India, where Accredited Social Health Activists (ASHAs) contribute to improving maternal and child health outcomes. By examining the role of ASHAs and the impact of financial incentives, this research aims to provide insights that can optimize CHW programs and address socio-economic barriers to healthcare access. This dissertation comprises three interconnected studies that examine the role of ASHAs in improving maternal and child health outcomes in India. Utilizing data from the Indian National Family Health Survey (2019-2021), the studies employ robust analytical methods, including multivariable logistic regression, Propensity Score Matching, Generalized Estimating Equations, and inverse probability weighting regression adjustment. The first study investigated the socio-demographic determinants associated with ASHA service utilization and predictors of institution-based births. The second study assessed the association between use of ASHA services on the receipt of benefits from the Janani Suraksha Yojana (JSY). The third study explores the effect of additional state-level financial incentives for ASHAs on maternal service utilization, including institution-based deliveries, complete antenatal care, and full immunization for children aged 12-24 months. Findings show the use of ASHA services improve maternal and child health outcomes, increasing the likelihood of institution-based deliveries by 1.6 times (95% CI: 1.51-1.65) and receipt of the JSY benefit by 1.3 times (95% CI:1.3-1.4). States offering supplementary incentives to ASHAs report higher utilization of key maternal health services. However, the impact of varying incentive amounts remains inconclusive. This research highlights the positive role of ASHAs in enhancing maternal and child healthcare access, thereby supporting Sustainable Development Goals 3 (Good Health and Well-being) and 10 (Reduced Inequalities). Further research is needed to optimize financial incentives. This research offers important insights for scaling CHW programs in India and other LMICs to improve maternal and child health outcomes globally.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".