The impact of social media coverage on attitudes towards mental illness and violent offending
Bibliographic record
Abstract
The aim of this study is to better understand stigma towards individuals with mental illness who commit violent offences, and examine ways to mitigate the negative impact of social media news stories of schizophrenia and violent offending. Psychology undergraduate students (N = 255) were exposed to Instagram images and captions of recent real news stories of violent offending by individuals with schizophrenia. In the experimental condition, contextual clinical explanatory information was integrated. Pre- and post-measures of stigma were completed. There was a significant increase in negative attitudes towards individuals with mental illness who committed violent offences following the no-context condition, which was clearly mitigated in the experimental condition where context was provided. In both conditions, there were significant increases in intended social-distancing behaviours towards and perceptions of dangerousness of individuals with schizophrenia, and negative beliefs about mental illness more generally. There appears to be utility in incorporating knowledge-based clinical information to mitigate some facets of stigma.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".