Customized Avatars In A Multiplatform Game On Mobile And Virtual Reality For Hospitalized Children In Hemato-Oncology: A Conceptual Design
Bibliographic record
Abstract
The hospital is an environment that may induce both anxiety and pain in hospitalized children and their families. The use of games and distraction offers a solution to better manage anxiety and pain. The objective of the present paper is to introduce “A Friend for Life,” a multi-platform game based on the creation of customized avatars to help children hospitalized in the hematology-oncology unit cope with their everyday pain and anxiety. This smartphone and virtual reality application uses the IKEA Effect and transmedia storytelling to improve child engagement in the game. This game was developed as part of AVATAR, a research project that combines collaborative action research with co-design. The project is led by our multidisciplinary team with relevant experience in nursing, pediatrics, hematology-oncology, and new technology. Also, our team includes a former patient and a former patient’s parent. In this paper, we introduce our serious game, “A Friend for Life”, and the collaborative development method that was used in the prototyping phase. Additionally, we will outline our hypothetical conceptual framework and its underlying concepts. Finally, we discuss the game's limitations and directions for future research.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".