Conceptual framework for designing video games for children with type 1 diabetes
Bibliographic record
Abstract
OBJECTIVE: to present a theoretically based conceptual framework for designing video games for children with type 1 diabetes mellitus. METHODS: this was a methodological study that developed a conceptual framework with nine steps in view of health behavior change theories and the user-centered design approach as theoretical and methodological frameworks, respectively. Twenty-one children, aged 7 to 12 years, participated by expressing their needs and preferences related to diabetes and video games. Data were analysed following content analysis guidelines. Then, a choice of appropriate health behavioral change theories and their determinants that should be capable of influencing children's behaviors and preferences. RESULTS: the conceptual framework proposes a video game that consists of six phases, each addressing one stage of behavioral change and specific determinants, aligned with the needs and preferences identified by the participating children. This study shows the applicability of this framework in view of each proposed phase presenting examples and the children's ideas. CONCLUSION: the results of this study contribute to advance the discussion on how behavioral theories and their determinants should be related to the design of creative and funny video games considering the profile of the target population as well as its needs and preferences.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".