Atmiyata, a community-led intervention to address common mental disorders: Study protocol for a stepped wedge cluster randomized controlled trial in rural Gujarat, India
Bibliographic record
Abstract
BACKGROUND: While lay-health worker models for mental health care have proven to be effective in controlled trials, there is limited evidence on the effectiveness and scalability of these models in rural communities in low- and middle-income countries (LMICs). Atmiyata is a rural community-led intervention using local community volunteers, called Champions, to identify and provide a package of community-based interventions for mental health, including evidence-based counseling for persons with common mental disorders (CMD). METHODS: The impact of the Atmiyata intervention is evaluated through a stepped wedge cluster randomized controlled trial (SW-CRCT) with a nested economic evaluation. The trial is implemented across 10 sub-blocks (645 villages) in Mehsana district in the state of Gujarat, with a catchment area of 1.52 million rural adults. There are 56 primary health centers (PHCs) in Mehsana district and villages covered under these PHCs are equally divided into four groups of clusters of 14 PHCs each. The intervention is rolled out in a staggered manner in these groups of villages at an interval of 5 months. The primary outcome is symptomatic improvement measured through the GHQ-12 at a 3-month follow-up. Secondary outcomes include: quality of life using the EURO-QoL (EQ- 5D), symptom improvement measured by the Self-Reporting Questionnaire-20 (SRQ-20), functioning using the World Health Organization's Disability Assessment Scale (WHO-DAS-12), depression symptoms using the Patient Health Questionnaire (PHQ-9), anxiety symptoms using Generalized Anxiety Disorder Questionnaire (GAD-7), and social participation using the Social Participation Scale (SPS). Generalized linear mixed effects model is employed for binary outcomes and linear mixed effects model for continuous outcomes. A Return on Investment (ROI) analysis of the intervention will be conducted to understand whether the intervention generates any return on financial investments made into the project. DISCUSSION: Stepped wedge designs are increasingly used a design to evaluate the real-life effectiveness of interventions. To the best of our knowledge, this is the first SW-CRCT in a low- and middle-income country evaluating the impact of the implementation of a community mental health intervention. The results of this study will contribute to the evidence on scaling-up lay health worker models for mental health interventions and contribute to the SW-CRCT literature in low- and middle-income countries. TRIAL REGISTRATION: The trial is registered prospectively with the Clinical Trial Registry in India and the Clinical Trial Registry number- CTRI/2017/03/008139. URL http://ctri.nic.in/Clinicaltrials/regtrial.php?modid=1&compid=19&EncHid=70845.17209. Date of registration- 20/03/2017.
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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 | 0.016 | 0.014 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.049 | 0.006 |
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".