Stigmatization of severe mental illness in India: Against the simple industrialization hypothesis
Bibliographic record
Abstract
BACKGROUND: Major international studies on course and outcome of schizophrenia suggest a better prognosis in the rural world and in low-income nations. Industrialization is thought to result in increased stigma for mental illness, which in turn is thought to worsen prognosis. The lack of an ethnographically derived and cross-culturally valid measure of stigma has hampered investigation. The present study deploys such a scale and examines stigmatizing attitudes towards the severely mentally ill among rural and urban community dwellers in India. AIM: To test the hypothesis that there are fewer stigmatizing attitudes towards the mentally ill amongst rural compared to urban community dwellers in India. MATERIALS AND METHODS: An ethnographically derived and vignette-based stigmatization scale was administered to a general community sample comprising two rural and one urban site in India. Responses were analyzed using univariate and multivariate statistical methods. RESULT: Rural Indians showed significantly higher stigma scores, especially those with a manual occupation. The overall pattern of differences between rural and urban samples suggests that the former deploy a punitive model towards the severely mentally ill, while the urban group expressed a liberal view of severe mental illness. Urban Indians showed a strong link between stigma and not wishing to work with a mentally ill individual, whereas no such link existed for rural Indians. CONCLUSION: This is the first study, using an ethnographically derived stigmatization scale, to report increased stigma amongst a rural Indian population. Findings from this study do not fully support the industrialization hypothesis to explain better outcome of severe mental illness in low-income nations. The lack of a link between stigma and work attitudes may partly explain this phenomenon.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".