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 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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".