Investigating violence against <i>Accredited Social Health Activists</i> (ASHAs): a mixed methods study from rural North Karnataka, India
Bibliographic record
Abstract
Background Accredited Social Health Activists (ASHAs) are female community health workers who primarily work to improve local reproductive, maternal, neonatal, and child health across India. As ASHAs often hail from patriarchal environments and are positioned at the bottom of the healthcare hierarchy, they are vulnerable to experiencing different forms of violence from the various individuals that they interact with. There is a gap in knowledge about the violence ASHAs experience. The purpose of this study was to assess the working condition of ASHAs, the extent and types of violence they experienced, and the corresponding perpetrators of this violence in two districts of Northern Karnataka. Methods Using a mixed methods approach, we first surveyed 396 ASHAs to characterize their experiences of violence. We then conducted in-depth interviews with 16 ASHAs to elaborate on survey findings. Data was analyzed using quantitative prevalence statistics and qualitative thematic analysis. Results The majority of ASHAs reported economic (88%) or emotional violence (73%), while many ASHAs reported sexual (32%) or physical violence (26%). ASHAs reported high levels of economic violence from their beneficiaries and their beneficiaries’ families (64%), emotional violence from their co-workers (44%), and physical and sexual violence from their husbands (17% and 12% respectively). Mixed methods findings revealed that violence was often rooted from their low positioning on the healthcare hierarchy, a lack of respect from community members, and limited autonomy at home. Conclusions Evidence from this study suggests that violence perpetrated against ASHAs is highly prevalent, diverse in forms, and often arises from the ASHA’s immediate circles. Interventions aiming to decrease violence against ASHA workers requires multi-level approach, with collaborative components empowering ASHAs, sensitizing ASHA families and co-workers, implementing regulations at the health facility level, and increasing community-wide respect for ASHAs and their role in the health care.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".