Community Attitudes toward People with Schizophrenia
Bibliographic record
Abstract
OBJECTIVE: We surveyed public attitudes toward people with schizophrenia as part of a pilot project for the World Psychiatric Association's Global Campaign to Fight Stigma and Discrimination Because of Schizophrenia. METHODS: We conducted random-digit telephone surveys with 1653 respondents (aged 15 years or over) residing in 2 adjacent rural and urban health regions (71.9% response rate). A brief interview collected information on experiences with people with a mental illness or schizophrenia, knowledge of causes and treatments for schizophrenia, and levels of social distance felt toward people with schizophrenia. RESULTS: One-half of the sample had known someone treated for schizophrenia or another mental illness. Of those able to identify a cause of schizophrenia (two-thirds), most identified a biological cause, usually a brain disease. Social distance increased with the level of intimacy required. One in 5 respondents thought they would be unable to maintain a friendship with, one-half would be unable to room with, and three-quarters would be unable to marry, someone with schizophrenia. Those over 60 were least knowledgeable or enlightened and the most socially distancing. Greater knowledge was associated with less-distancing attitudes. When other factors were controlled, exposure to the mentally ill was not correlated with knowledge or attitudes, even among those who had worked in agencies providing services to the mentally ill. CONCLUSIONS: Most respondents were relatively well informed and progressive in their reported understanding of schizophrenia and its treatment. Clear subgroup differences were apparent with respect to age and knowledge. Knowledge of schizophrenia, not exposure to the mentally ill, was a central modifiable correlate of stigma.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".