A Pilot Study of the effect of exposure to Stand-up Comedy Performed by People With Mental Illness on Medical Students' Stigmatization of Affected Individuals
Bibliographic record
Abstract
Objective: Previous work shows that many medical professionals hold stigmatizing attitudes towards people with mental illnesses. Medical professionals’ stigmatizing attitudes have been associated with decreased use of needed healthcare services among individuals with mental illness; and this can exacerbate the effects and symptoms of the illness on the individual. Medical professionals’ attitudes are perhaps best modified early in their training. Thus, we aimed to determine whether a novel intervention could decrease medical students’ stigmatizing attitudes towards people with mental illness. Methods: Students attended a presentation about a program which trains individuals with mental illness to perform stand-up comedy, then interacted with the comedians in small groups. Immediately before (T1) and after (T2) the intervention, participants self-rated their comfort with asking patients about mental illness, and completed scales measuring two aspects of stigma: stereotype endorsement, and broad negative attitudes towards people with mental illness. Results: T1 and T2 questionnaires were returned by 49 students. At T2, 52% reported feeling more comfortable asking patients about a history of mental illness. There was no change in broad attitudes towards mental illness, but endorsement of negative stereotypes about mental illness decreased significantly from T1 to T2 . Conclusions: These pilot data warrant further investigation of the effects of this novel intervention.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".