Perceptions of Traditional Healing for Mental Illness in Rural Gujarat
Bibliographic record
Abstract
BACKGROUND: Despite the significant toll of mental illness on the Indian population, resources for patients often are scarce, especially in rural areas. Traditional healing has a long history in India and is still widely used, including for mental illnesses. However, its use has rarely been studied systematically. OBJECTIVE: The aim of this study was to determine the perspective of patients, their families, and healthy community members toward faith healing for mental illness, including the type of interventions received, perceptions of its efficacy, and overall satisfaction with the process. We also sought to explore the range of care received in the community and investigate possibilities for enhancing mental health treatment in rural Gujarat. METHODS: We interviewed 49 individuals in July 2013 at Dhiraj General Hospital and in 8 villages surrounding Vadodara. A structured qualitative interview elicited attitudes toward faith healing for mental illnesses and other diseases. Qualitative analysis was performed on the completed data set using grounded theory methodology. FINDINGS: Subjects treated by both a doctor and a healer reported they overwhelmingly would recommend a doctor over a healer. Almost all who were treated with medication recognized an improvement in their condition. Many subjects felt that traditional healing can be beneficial and believed that patients should initially go to a healer for their problems. Many also felt that healers are not effective for mental illness or are dishonest and should not be used. CONCLUSIONS: Subjects were largely dissatisfied with their experiences with traditional healers, but healing is still an incredibly common first-line practice in Gujarat. Because healers are such integral parts of their communities and so commonly sought out, collaboration between faith healers and medical practitioners would hold significant promise as a means to benefit patients. This partnership could improve access to care and decrease the burden of mental illness experienced by patients and their communities.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".