Can participatory video reduce mental illness stigma? Results from a Canadian action-research study of feasibility and impact
Bibliographic record
Abstract
BACKGROUND: Evidence suggests that stigma against people with mental illness remains high. This demands innovative approaches to reduce stigma. One innovative stigma reduction method is participatory video (PV), whereby marginalized people come together to script, film and produce bottom-up educational videos about shared issues. These videos are then shown to target groups. This paper has two objectives (i) to examine the feasibility of using participatory video with people with severe mental illness (SMI); and (ii) to assess viewer impressions of the resultant videos and subsequent subjective impact. METHODS: We conducted a participatory action research study with three workgroups of people with severe mental illness situated in different Canadian cities, who set out to create and disseminate locally-grounded mental-health themed videos. This involved process and outcome evaluation to assess feasibility and impact. Specifically, we (i) observed fidelity to a co-designed action-plan in all three workgroups; (ii) distributed brief purpose-built questionnaires to viewers at organized screenings to assess preliminary impact; and (iii) conducted focus groups with viewers to elicit further impressions of the videos and subsequent subjective impact. RESULTS: The three workgroups achieved high-fidelity to the action-plan. They successfully produced a total of 26 videos, over double the targeted number, during an 18-month period. Likewise, the workgroups organized 49 screenings at a range of venues attended by 1542 people, again exceeding the action-plan targets. Results from the viewer questionnaires (N = 1104, response rate 72%) indicated that viewers reported that their understandings had improved after watching the videos. Four themes emerged from six viewer focus groups (N = 30), with participants frequently noting that videos were (i) educational and informative; (ii) real and relatable; (iii) attention-grabbing; and (iv) change-inducing. CONCLUSIONS: To our knowledge, this study is the first large-scale multi-site project examining the feasibility and impact of a participatory video program for people with severe mental illness. The results indicate that participatory video is a feasible method in this population and gives preliminary evidence that resultant videos can reduce viewer stigma. Thus, participatory video should be considered a promising practice in the ongoing effort to reduce mental illness 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 | 0.047 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".