A Pilot Study to Assess the Impact of a Multifactorial Explanation for Mental Illness on Prejudicial Attitudes Towards People With Mental Illness
Bibliographic record
Abstract
Public stigma and prejudice toward people with psychiatric conditions is highly prevalent and damaging. Explanations for the origins of mental illness can influence attitudes toward people with these conditions. To date, studies exploring the effects of explanations for the origins of mental illness have focused on genetic or environmental explanations, and the impact of evidence-based multifactorial explanations for psychiatric illness on public attitudes remains unknown. Participants were recruited through Amazon Mechanical Turk to watch a 4-min video about the "mental illness jar model"-an evidence-based analogy that explains the complex interactions between genes and environment in the development of mental illness. Participants provided demographic information and completed questions regarding knowledge about the causes of mental illness, and the Prejudice towards People with Mental Illness (PPMI) scale both before and after watching the video. A total of 106 eligible participants completed the study. Watching the video had no significant effect on participants' knowledge about the causes of mental illness (p = 0.06), but there was a significant decrease in prejudicial attitudes toward mental illness (p = 0.0003), the effect size was small (-0.15). The use of this brief video (available at cogastudy.org) is a promising tool to decrease prejudicial attitudes toward mental illness that warrants further study.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.009 | 0.002 |
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".