Knowledge and Attitudes about Schizophrenia among Employers in Japan
Bibliographic record
Abstract
PURPOSE: A high percentage of schizophrenia patients cannot find work. If these patients are to find long-term employment, it is essential that employers understand schizophrenia. Therefore, this study aimed to assess knowledge and attitudes about schizophrenia among employers in Japan.METHODS: A total of 1877 executives were recruited from private companies to examine knowledge and attitudes about schizophrenia, awareness of employment support, and likelihood of hiring schizophrenic patients. Higher scores indicated greater knowledge and/or higher levels of stigma.RESULTS: Small-scale entrepreneurs were significantly less likely to believe that they might be able to employ a schizophrenia patient. They tended to regard mentally ill people, including schizophrenia patients, as dangerous, despite having little or no contact with them. Basic knowledge of schizophrenia was significantly higher (p = 0.001) and average scores on a number of attitude measures significantly lower (p = 0.001) for employers who said they might employ people with schizophrenia than those that didn’t. More than 83.5% of respondents were unaware of support available for people with mental illnesses. Half expressed desire for support from outside agencies in hiring and ongoing employment of people with schizophrenia.CONCLUSIONS: This study identified a particular group of employers who were very unlikely to employ anyone with schizophrenia. This might enable targeted interventions to change attitudes among this group. Also found was a widespread lack of knowledge of support available for employers and employees, suggesting that more public information about this may be helpful in increasing employment among those with mental health problems.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".