Promoting wellbeing and improving access to mental health care through community champions in rural India: the Atmiyata intervention approach
Bibliographic record
Abstract
There are limited accounts of community-based interventions for reducing distress or providing support for people with common mental disorders (CMDs) in low and middle-income countries. The recently implemented Atmiyata programme is one such community-based mental health intervention focused on promoting wellness and reducing distress through community volunteers in a rural area in the state of Maharashtra, India. This case study describes the content and the process of implementation of Atmiyata and how community volunteers were trained to become Atmiyata champions and mitras (friends). The Atmiyata programme trained Atmiyata champions to provide support and basic counselling to community members with common mental health disorders, facilitate access to mental health care and social benefits, improve community awareness of mental health issues, and to promote well-being. Challenges to implementation included logistical challenges (difficult terrain and weather conditions at the implementation site), content-related challenges (securing social welfare benefits for people with CMDs), and partnership challenges (turnover of public health workers involved in referral chain, resistance from public sector mental health specialists). The case study serves as an example for how such a model can be sustained over time at low cost. The next steps of the programme include evaluation of the impact of the Atmiyata intervention through a pre-post study and adapting the intervention for further scale-up in other settings in India.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".