An Analysis of General Public and Professional's Attitudes about Mental Health Courts: Predictors of a Positive Perspective
Bibliographic record
Abstract
Little research has been conducted on the public perception of mental health courts (MHCs) despite its potential to influence government support and funding. To measure public and professionals’ opinions about MHCs, self-report attitude measures were administered online to members of the general public ( n = 272) and professional groups with previous employment-related exposure to persons with mental health issues ( n = 237). Over 86% of the professional exposure sample had positive attitudes towards MHCs, whereas only 4% reported negative opinions. Approximately 70% of professionals supported government funding for a MHC in their community and 57% agreed even if this led to a tax increase. The public sample was also generally positive in their opinions, and only 3% reported negative opinions. Approximately 80% of public sample reported that they would support or strongly support government funding for a MHC in their community, and 58% would support it even if it led to a tax increase. Positive attitudes were predicted by prior knowledge of MHCs, older age, exposure to mental-health coursework, psychological openness, positive help-seeking attitudes, and positive attitudes towards mental illness in general. These patterns were similar, but varying in degree, for those with and without employment-related exposure to mental illness. Education about the effect of a specific MHC led to significant changes in the proportion of favourable opinions. Thus, public perception of MHCs ranged from neutral to positive and can be enhanced through education.
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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 | 0.002 | 0.010 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".