AI for Personalized Mental Health Support – Early Intervention in Rural/Underserved India
Bibliographic record
Abstract
In rural areas of India that lack proper support, the limited availability of mental health services, problems in connectivity, and the stigma of the society greatly slow down the process of mental health diagnosis and intervention. As the case is with various psychological disorders such as stress, anxiety, and burnout. Our proposal is to implement an integrated AI-powered system consisting of risk prediction through machine learning along with a culturally adjusted langauge independent conversational bot that supports English and Tamil. We have developed a large-scale, localized dataset supplemented by audio that allows the system to be a personal, and the most suitable mental health guide for the users of low-resource settings. The system uses Random Forest classifiers with a maximum accuracy of 98.2% along with a LLaMA(Large Language Model Meta AI) for empathetic interactions. The system intends to address early stage mental health issues in remote areas, break down the stigma, and motivate people to look for the help they need. Expansion of the system to include wearable data and real-time adaptive feedback, as well as pilot deployment and clinical validation, are some of the future steps envisaged to further deepen the reach of scalable mental health support in rural 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".