Public Stigma Related to People With Mental Health Conditions Among Japanese Company Employees
Bibliographic record
Abstract
Background: The public stigma related to mental illness is the general public's negative misconceptions about people with mental health conditions (PMHCs). The public stigma of mental illness is detrimental to PMHC as it leads to loss of opportunities and unemployment. The aim of the study was to clarify the status of public stigma related to PMHC, focusing on knowledge about mental illness and social distance concerning PMHC. Methods: A survey was conducted among 970 Japanese office workers aged 20 to 60 years. Accurate knowledge of mental illness was assessed using the Mental Illness and Disorder Understanding Scale (MIDUS). The Attitudinal Social Distance (ASD) was used to determine social distance in relation to PMHC. The demographic characteristics of the participants evaluated were sex, age group measured in years, employment position, employment status, and attendance at mental illness stigma training. Results: Regular employees (P = 0.03) and those having prior contact experience (P = 0.01) had more accurate knowledge. Participants between 50 to 59 years old (M = 15.87, standard deviation (SD) = 3.35) had greater social distance than those under 30 years old (M = 14.78, SD = 3.97, P < 0.05). The results of multiple linear regression analysis found that employment status (partial r = -0.07, P < 0.05) and prior contact experience (partial r = -0.15, P < 0.01) significantly affected the MIDUS score, whereas no variable had a significant effect on the ASD score. Conclusions: Accurate knowledge of mental illness was significantly higher among regular employees and those with contact experience. Social distance was significantly lower among those under the age of 30 years.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".