Optimizing Participant Engagement in Cyberhealth Co-Design: Course-of-Action Framework Analysis
Bibliographic record
Abstract
Background: Co-design is recognized for its potential to enhance the usability of products through active user participation. However, participation alone does not guarantee the effectiveness of the resulting product. Understanding participants' engagement during co-design activities can provide valuable insights into their motivations, concerns, and contributions, which are critical to achieving successful outcomes. Objective: This study aims to analyze participant engagement in a digital health co-design parent project focusing on developing a tool to facilitate support-seeking for elderly caregivers. Methods: The parent project included 74 participants from 3 categories: caregivers, health care and social service professionals, and community workers. Testimonies for this study were collected from 20 participants using the self-confrontation interview methodology. Engagement was analyzed qualitatively using the course-of-action framework. The engagements were organized into emergent themes. The analysis focused on variations in engagement patterns across participant categories and sessions. Results: A total of 3 themes of engagement were identified: tool design, participant needs, and contextual situations. Engagement was distributed similarly across themes, except for community workers, who were more focused on needs (52/94, 42%) than tool design (25/62, 20%). There was significant variation in engagement over sessions, with tool design being more prominent during specific sessions (co-design sessions CoD5, CoD7, and CoD8) and less important during others (CoD4, AC2 [advisory committee session], CoD6, and AC3). Activities directly tied to design tasks significantly enhanced engagement with tool design. These results underscore the influence of activity types in shaping participants' focus and involvement. Conclusions: This study highlights the role of affordances in co-design activities to balance engagement across design, collaboration, and participation dimensions. By strategically leveraging affordances, future co-design projects can optimize engagement and ensure more effective outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".