Program for Promoting the Employment of Schizophrenic Patients in Japan
Bibliographic record
Abstract
In Japan, a large proportion of schizophrenic patients cannot find work. Accordingly, it is necessary to promote positive attitudes among employers about hiring such patients. However, few programs in Japan educate employers about schizophrenia and there is little evaluation of such programs. Our study participants were 1,175 executives in private enterprises who registered with an Internet questionnaire survey company. The participants in the intervention group viewed an educational video developed to increase understanding about schizophrenia. This longitudinal study examined how employers’ attitudes about hiring schizophrenic patients changed before and after watching the video. The number of respondents from both the intervention and non-intervention groups who responded that they did not understand how to employ and manage schizophrenics and so would not hire them showed a significant increase at 1 week after baseline (p = 0.001); however, there was a significant increase at 3 years after baseline only in the non-intervention group (p = 0.019). Only in the non-intervention group did Social Distance Scale-Japanese version scores show a significant decrease at 1 week after baseline (p = 0.011); they increased significantly from 1 week after to 3 years after baseline (p = 0.001). Our educational intervention aimed to promote employers’ willingness to employ schizophrenic patients. However, to reduce stigma and increase such willingness, our program could be improved.
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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 | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".