“Participation Is Fun and Empowering”: A Participatory Approach to Co-Design a Cultural Art Program for Older Chinese at Risk of Depression in Hong Kong
Bibliographic record
Abstract
Background and Objectives: Internalized ageism and stigma of mental illness may disempower older people and impede help-seeking among those at risk of depression. Arts are deemed enjoyable, stigma-free, and conducive to mental health, and a participatory approach can engage and empower potential service users. This study aimed to co-design a cultural art program and test its feasibility in empowering older Chinese people in Hong Kong and preventing depression. Research Design and Methods: Adopting a participatory approach and guided by the Knowledge-to-Action framework, we co-designed a 9-session group art program using Chinese calligraphy as the channel for gaining emotional awareness and facilitating expression. The iterative participatory co-design process engaged 10 older people, 3 researchers, 3 art therapists, and 2 social workers through multiple workshops and interviews. We tested the program's acceptability and feasibility in 15 community-dwelling older people at risk of depression (mean age = 71.6). Mixed methods were used, including pre- and postintervention questionnaires, observation, and focus groups. Results: < .05), but not in other mental health-related measurements. Participants reflected that active participation and learning new art skills were fun and empowering, arts enabled them to gain insight into and express deeper feelings, and groups with peers made them feel relatable and accepted. Discussion and Implications: Culturally appropriate participatory arts groups can effectively promote empowerment in older people, and future research should balance eliciting meaningful personal experiences and measurable changes.
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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 | 0.013 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".