Under the banyan tree - exclusion and inclusion of people with mental disorders in rural North India
Bibliographic record
Abstract
BACKGROUND: Social exclusion is both cause and consequence of mental disorders. People with mental disorders (PWMD) are among the most socially excluded in all societies yet little is known about their experiences in North India. This qualitative study aims to describe experiences of exclusion and inclusion of PWMD in two rural communities in Uttar Pradesh, India. METHODS: In-depth interviews with 20 PWMD and eight caregivers were carried out in May 2013. Interviews probed experiences of help-seeking, stigma, discrimination, exclusion, participation, agency and inclusion in their households and communities. Qualitative content analysis was used to generate codes, categories and finally 12 key themes. RESULTS: A continuum of exclusion was the dominant experience for participants, ranging from nuanced distancing, negative judgements and social isolation, and self-stigma to overt acts of exclusion such as ridicule, disinheritance and physical violence. Mixed in with this however, some participants described a sense of belonging, opportunity for participation and support from both family and community members. CONCLUSIONS: These findings underline the urgent need for initiatives that increase mental health literacy, access to services and social inclusion of PWMD in North India, and highlight the possibilities of using human rights frameworks in situations of physical and economic violence. The findings also highlight the urgent need to reduce stigma and take actions in policy and at all levels in society to increase inclusion of people with mental distress and disorders.
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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 | 0.010 | 0.009 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".