Including the voice of paediatric patients: Cocreation of an engagement game
Bibliographic record
Abstract
BACKGROUND: Engaging patients in health care, research and policy is essential to improving patient-important health outcomes and the quality of care. Although the importance of patient engagement is increasingly acknowledged, clinicians and researchers still find it difficult to engage patients, especially paediatric patients. To facilitate the engagement of children and adolescents in health care, the aim of this project is to develop an engagement game. METHODS: A user-centred design was used to develop a patient engagement game in three steps: (1) identification of important themes for adolescents regarding their illness, treatment and hospital care, (2) evaluation of the draft version of the game and (3) testing usability in clinical practice. Adolescents (12-18 years) were engaged in all steps of the development process through focus groups, interviews or a workshop. These were audio-recorded, transcribed verbatim and analysed in MAXQDA. RESULTS: (1) The important themes for adolescents (N = 15) were included: visiting the hospital, participating, disease and treatment, social environment, feelings, dealing with staff, acceptation, autonomy, disclosure and chronically ill peers. (2) Then, based on these themes, the engagement game was developed and the draft version was evaluated by 13 adolescents. Based on their feedback, changes were made to the game (e.g., adjusting the images and changing the game rules). (3) Regarding usability, the pilot version was evaluated positively. The game helped adolescents to give their opinion. Based on the feedback of adolescents, some last adjustments (e.g., changing colours and adding a game board) were made, which led to the final version of the game, All Voices Count. CONCLUSIONS: Working together with adolescents, All Voices Count, a patient engagement game was developed. This game provides clinicians with a tool that supports shared decision-making to address adolescents' wishes and needs. PATIENT OR PUBLIC CONTRIBUTION: Paediatric patients, clinicians, researchers, youth panel of Fonds NutsOhra and patient associations (Patient Alliance for Rare and Genetic Diseases, Dutch Childhood Cancer Organization) were involved in all phases of the development of the patient engagement game-from writing the project plan to the final version of the game.
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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 | 0.003 | 0.010 |
| 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 | 0.003 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".