A Co-Design Protocol for a Serious Game Supporting Emotion Regulation in Children
Bibliographic record
Abstract
Technological advancements have led to the development of serious games (SGs), which are now widely used in mental healthcare. These digital interventions are used for education and treatment and are effective in supporting emotion regulation (ER) in young people. Their engaging nature and accessibility make them a promising solution to gaps in traditional mental health services. This work presents a protocol for the development of a SG designed to promote emotional knowledge and ER skills in school-aged children in both school and clinical settings. The SG’s development will be based on a co-design methodology involving the active participation of children, parents, teachers, and healthcare professionals throughout three phases: 1) conceptual design, 2) prototype testing, and 3) usability testing. The outcomes of SG will be assessed based on user experience and satisfaction, as well as system usability and clinical measures. Pre- and post-test assessments will be used to measure improvements in emotion recognition and ER skills. The final SG is expected to fulfill real-world needs and ensure that it is educational and enjoyable. This protocol provides a replicable framework for co-designed digital interventions in mental health, emphasizing user-centered design to enhance both effectiveness and involvement.
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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 | 0.045 | 0.059 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.079 | 0.014 |
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".