Using theatre to address mental illness stigma: a knowledge translation study in bipolar disorder
Bibliographic record
Abstract
BACKGROUND: Reduction of the stigma of mental illness is an international priority; arts- and contact-based approaches represent a promising mode of intervention. This project was designed to explore the impact of a one-woman theatrical performance on attitudes towards bipolar disorder (BD) on people with BD and healthcare providers. METHODS: A playwright and actress who lives with BD developed a stage performance - 'That's Just Crazy Talk' - targeting stigmatizing attitudes towards BD. Prospective, longitudinal and sequential mixed methods were used to assess the impact of the performance on people with BD (n = 80) and healthcare providers (n = 84). Qualitative interviews were conducted with 33 participants (14 people with BD and 19 healthcare providers). RESULTS AND DISCUSSION: Quantitatively, healthcare providers showed significantly improved attitudes immediately post-performance, but this change was not maintained over time; people with BD showed little quantitative change. Qualitatively, both people with BD and BD healthcare providers showed enduring and broadly positive changes. A theatrical presentation designed to reduce stigma produced immediate impact on healthcare providers quantitatively and significant qualitative impact on people with BD and healthcare providers. Additionally, the utility of using mixed-method approaches in mental health research was demonstrated.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".