I do not have stigma towards people with ADHD (but I do think they’re lazy): Using education and experience to reduce negative attitudes towards ADHD
Bibliographic record
Abstract
The current project examined the explicit, implicit, and social distance attitudes towards people with Attention-Deficit/Hyperactivity Disorder (ADHD) and the effect of education- or contact-based anti-stigma video on attitudes towards ADHD. In Study 1, 294 undergraduate students completed measures of explicit, implicit, and social distance attitudes towards ADHD. Results indicated significantly more negative explicit, implicit, and social distance attitudes towards people with ADHD compared to a comparison target of Asthma, and complex relationships between these variables. In Study 2, 299 undergraduate students were randomly assigned to watch a control, education, or contact video with a male or female actor with lived experience of ADHD. They then completed the same measures as in Study 1. Results indicated that negative explicit attitudes towards people with ADHD relative to Asthma were significantly lower after the education, and not contact, video. The intervention did not significantly affect the responses on the implicit or social distance measures. Additionally, only the male, and not the female, education video resulted in lower explicit scores. The results of the current project suggest that while there are negative attitudes towards people with ADHD, they can be improved with a short, video-based intervention. This project has significant implications for university students and their education about and treatment of people with ADHD. It also has significant implications for the development and utilization of anti-stigma interventions not only towards people with ADHD, but for mental illness as a whole.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".