Efficacy of a Photovoice‐based video as an online mental illness anti‐stigma intervention and the role of empathy in audience response: A randomized controlled trial
Bibliographic record
Abstract
Abstract This study examined the efficacy of a Photovoice‐based video as a novel online anti‐stigma video in reducing mental illness stigma, as well as the role of empathic concern in stigma reduction. Photovoice is a grassroots process by which members of a marginalized group, such as people with a mental illness, document and convey their experience; in this study’s context, the experience of living with a mental illness and the stigma associated with this experience. Canadian undergraduate university students (n = 303; average age = 21) were randomly assigned to view either a Photovoice‐based anti‐stigma intervention video (n = 156) or a control video (n = 147). Compared to the control condition, the Photovoice‐based video was efficacious in reducing mental illness stigma, including reduced fear and anger toward people with a mental illness, decreased perceptions of dangerousness, and decreased desired social distance. In addition, the intervention was efficacious in maintaining reduced desired social distance relative to the control at 1‐month post‐intervention. Finally, empathic concern was found to mediate the relationship between the Photovoice‐based video and reduced mental illness stigma, suggesting that one way the intervention reduced mental illness stigma was by eliciting empathy in the viewer.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".