Stigma and Discrimination in Schizophrenia
Bibliographic record
Abstract
Title: Stigma and Discrimination: The Mumbai Experience Main Author: Amresh Shrivastava MD, DPM, MRCPsych, University of Western Ontario, London, Ontario, Canada. Co-Authors: Gopa Sarkhel, MA, Iyer Sunita MA, Thakar Meghana MA, Shah Nilesh, MD, DPM Address of Presenter: Executive Director, Mental health foundation of India (PRERANA Charitable Trust) Mumbai, India; Currently at Department of Psychiatry, The University of Western Ontario, London, Ontario, Canada Address: Regional mental health care, 467 Sunset Drive, St. Thomas, N5N 3V9, St. Thomas, Ontario, Canada Phone: 1-5196318510; Fax: 1-519-631-2512. E-mail: amresh.edu@gmail.com Background: People with schizophrenia suffer from stigma and discrimination to a great extent, and this continues to be a major factor preventing treatment and social integration. This study was part of the WPA’s ‘Open-the-doors’ global anti-stigma programme. Aims: The main objective was too describe the manifestations and impact of stigma in family members and patients with schizophrenia in the metropolis of Mumbai. Method: A semi-structured survey was given to the members of three hundred families of schizophrenics attending a carer's meeting—an ongoing psycho-educational and self help support group activity. Two hundred and twenty four responded (75% response). Results: Results showed that stigma and discrimination were present in social, personal, and occupational spheres. It caused poor self-esteem (69%), discrimination within families (50%), marital problems (44%), and employment discrimination (42%). It mostly manifested in the form of neglect (61%), offensive comments (46%), and negative projections in the media (32%). A major source of stigma was identified as the general community (69%), leading to psychosocial consequences such as hiding the illness (38%), lack of acceptance in families (31%), refusal for jobs (27%), social exclusion (23%), and social harassment (14%). Conclusions: Stigma and discrimination affected all areas of life and caused negative psycho-social impacts.
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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 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| 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".