Audience Response to Photovoice as a Mental Illness Antistigma Intervention
Bibliographic record
Abstract
The present study examined the effectiveness and efficacy of a novel antistigma intervention in reducing mental illness stigma, as well as the role of audience empathy as a mediator of stigma reduction following antistigma intervention. Study 1 examined the effectiveness of an antistigma intervention developed through grassroots collaboration between the Canadian Mental Health Association and individuals that have experienced mental illness. This intervention was unique in that it featured a multimodal format that combined psychoeducation, live contact, and a Photovoice video, which has not been examined as an antistigma intervention in the literature to date. Fifty-two students viewed the intervention and completed measures of mental illness stigma at both pre- and post-intervention. Results showed that participants reported decreased mental illness stigma from pre- to post-intervention. Study 2 built off of these findings to examine the efficacy of the Photovoice video as a standalone online antistigma intervention. Online antistigma videos have not been widely researched in the literature, despite the low-cost and dissemination benefits associated with an online video format. Three hundred and three students were randomly assigned to either the Photovoice video (n = 156) or a control video (n = 147). Results indicated that the Photovoice video was efficacious in reducing mental illness stigma, including reduced fear, anger, perceived dangerousness, and desired social distance between pre- and post-intervention, relative to the control. In addition, 104 participants (Photovoice = 56; control = 48) returned to complete follow-up measures at 1-month post-intervention. Photovoice was efficacious in maintaining reduced desired social distance relative to the control, indicative of a continued willingness to interact with individuals that have a mental illness. Finally, viewer empathy was found to mediate the relationship between the Photovoice intervention and reduced mental illness stigma, suggesting that the Photovoice video reduced mental illness stigma by eliciting empathy in the viewer. Implications for the development of antistigma interventions are discussed, as well as limitations of the study and directions for future research.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".