The Value of Children's Voices for a Video Game Development in the Context of Type 1 Diabetes: Focus Group Study
Bibliographic record
Abstract
BACKGROUND: Children with type 1 diabetes mellitus (T1DM) face daily challenges performing self-care tasks, controlling symptoms, and dealing with psychosocial issues. The use of video games to improve health is a successful support for persons with chronic diseases, promoting adequate self-management through simulations of real life. Involving future users in the development of games is essential to generating innovative, creative, and effective programs. OBJECTIVE: Our goal is to identify what children with T1DM need to know about their disease and their self-care tasks as well as their preferences in video games. METHODS: Children with T1DM provided input about their learning needs, self-care tasks, and preferences in video games. Three categories were identified through qualitative content analysis: dealing with emotions and knowledge, practical skills and awareness, and game preferences. RESULTS: Children expressed concerns about the difficulties of self-care, lack of knowledge about diabetes, and lack of awareness about the consequences of behaviors related to self-care, which contribute to inappropriate behaviors and significantly impact self-management of their disease. They expressed enthusiasm for a video game for children with diabetes that considered their needs and preferences. CONCLUSIONS: Findings support the potential benefits when children's input is considered in game design. Consideration of customer needs and preferences is a powerful resource in the development of video games with enhanced learning experience.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".