Association between the use of Accredited Social Health Activist (ASHA) services and uptake of institutional deliveries in India
Bibliographic record
Abstract
This study examines the impact of accredited social health activists (ASHAs), on increasing rates of institution-based deliveries among Indian women with a specific focus on the nine low-performing, empowered action group states and Assam (EAGA) in India. Using the latest round of the National Family Health Survey-V (2019-21), we first investigate the association between the use of ASHA services and socio-demographic attributes of women using a multivariate logistic regression. We then use propensity-score matching (PSM) to address observable selection bias in the data and assess the impact of ASHA services on the likelihood of institution-based deliveries using a generalized estimating equations model. Of the 232,920 women in our sample, 55.5% lived in EAGA states. Overall, 63.3% of women (70.6% in EAGA states) reported utilizing ASHA services, and 88.6% had an institution-based delivery (84.0% in EAGA states). Younger women from the poorest wealth index were more likely to use ASHA services and women in rural areas had a two-fold likelihood. Conversely, women with health insurance were less likely to use ASHA services compared to those without. Using PSM, the average treatment effect of using ASHA services on institution-based deliveries was 5.1% for all India (EAGA = 7.4%). The generalized estimating equations model indicated that the use of ASHA services significantly increased the likelihood of institution-based delivery by 1.6 times (95%CI = 1.5-1.7) for all India (EAGA = 1.8; 95%CI = 1.7-1.9). Our study finds that ASHAs are effective in enhancing the uptake of maternal services particularly institution-based deliveries. These findings underscore the necessity for continual, systematic investments to strengthen the ASHA program and to optimize the program's effectiveness in varied settings that rely on the community health worker model, thereby advancing child and maternal health outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".