Anticipated discrimination among people with schizophrenia
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to evaluate the level of anticipated discrimination in people with schizophrenia (n = 732) from 27 countries in the International Study of Discrimination and Stigma Outcomes (INDIGO). METHOD: Anticipated discrimination was assessed through four questions of Discrimination and Stigma Scale. Twenty-five individuals were identified at each site who were reasonably representative of all such treated cases within the local area. RESULTS: Sixty-four per cent of the participants reported that they had stopped themselves from applying for work, training or education because of anticipated discrimination. Seventy-two per cent of them reported that they felt the need to conceal their diagnosis. Expecting to be avoided by others who know about their diagnosis was highly associated with decisions to conceal their diagnosis. Those who concealed their diagnosis were younger and more educated. The participants who perceived discrimination by others were more likely to stop themselves from looking for a close relationship. Anticipated discrimination in finding and keeping work was more common in the absence than in the presence of experienced discrimination, and the similar findings applied to intimate relationships. CONCLUSION: This study shows that anticipated discrimination among people with schizophrenia is common, but is not necessarily associated with experienced discrimination.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".