Mental Health Care Support in Rural India
Bibliographic record
Abstract
Importance: More than 150 million people in India need mental health care but few have access to affordable care, especially in rural areas. Objective: To determine whether a multifaceted intervention involving a digital health care model along with a community-based antistigma campaign leads to reduced depression risk and lower mental health-related stigma among adults residing in rural India. Design, Setting, and Participants: This parallel, cluster randomized, usual care-controlled trial was conducted from September 2020 to December 2021 with blinded follow-up assessments at 3, 6, and 12 months at 44 rural primary health centers across 3 districts in Haryana and Andhra Pradesh states in India. Adults aged 18 years and older at high risk of depression or self-harm defined by either a Patient Health Questionnaire-9 item (PHQ-9) score of 10 or greater, a Generalized Anxiety Disorder-7 item (GAD-7) score of 10 or greater, or a score of 2 or greater on the self-harm/suicide risk question on the PHQ-9. A second cohort of adults not at high risk were selected randomly from the remaining screened population. Data were cleaned and analyzed from April 2022 to February 2023. Interventions: The 12-month intervention included a community-based antistigma campaign involving all participants and a digital mental health intervention involving only participants at high risk. Primary health care workers were trained to identify and manage participants at high risk using the Mental Health Gap Action Programme guidelines from the World Health Organization. Main Outcomes and Measures: The 2 coprimary outcomes assessed at 12 months were mean PHQ-9 scores in the high-risk cohort and mean behavior scores in the combined high-risk and non-high-risk cohorts using the Mental Health Knowledge, Attitude, and Behavior scale. Results: Altogether, 9928 participants were recruited (3365 at high risk and 6563 not at high risk; 5638 [57%] female and 4290 [43%] male; mean [SD] age, 43 [16] years) with 9057 (91.2%) followed up at 12 months. Mean PHQ-9 scores at 12 months for the high-risk cohort were lower in the intervention vs control groups (2.77 vs 4.48; mean difference, -1.71; 95% CI, -2.53 to -0.89; P < .001). The remission rate in the high-risk cohort (PHQ-9 and GAD-7 scores <5 and no risk of self-harm) was higher in the intervention vs control group (74.7% vs 50.6%; odds ratio [OR], 2.88; 95% CI, 1.53 to 5.42; P = .001). Across both cohorts, there was no difference in 12-month behavior scores in the intervention vs control group (17.39 vs 17.74; mean difference, -0.35; 95% CI, -1.11 to 0.41; P = .36). Conclusions and Relevance: A multifaceted intervention was effective in reducing depression risk but did not improve intended help-seeking behaviors for mental illness. Trial Registration: Clinical Trial Registry India: CTRI/2018/08/015355.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".