Gamification strategies that promote leisure participation in children and youth with disabilities
Bibliographic record
Abstract
BACKGROUND: Participation in leisure activities is important for childhood development; however, youth with disabilities (YWD) experience participation limitations due to various barriers, including a lack of information about available adaptive and inclusive leisure activities. The Jooay mobile app, addresses this barrier by providing YWD, parents, and clinicians with information about adaptive and leisure activities in their neighborhoods across Canada. Game-like features, known as gamification, can enhance user engagement with mobile technologies and support health behavior change. OBJECTIVE: This study sought to explore the needs and perspectives of YWD, parents, clinicians, and community organizations about how gamification can increase their engagement with a mobile app that targets health behavior changes. MATERIALS AND METHODS: = 21) with YWD, parents, clinicians, and community organization representatives. Interviews were recorded and transcribed verbatim. Deductive and inductive approaches alongside constant comparative analysis were used. RESULTS: Qualitative analysis revealed four interconnected gamification strategies: (1) supporting participation goals; (2) bringing fun; (3) connecting with others; and (4) consideration for different users' experiences. We learned that mobile apps may have increased success in facilitating health behavior changes if they are tailored to individual preferences and may benefit from including personalized goal setting, a feedback system, such as a progress tracker, fun-related elements (especially for YWD), social networking for peer support, tailored user experiences for different user types, and expanded safety features to ensure privacy. CONCLUSIONS: These findings inform the inclusive and participatory design of digital health tools like Jooay, highlighting their potential to support behavior change, and promote meaningful participation in leisure activities.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".