A Norm-Creative Method for Co-constructing Personas With Children With Disabilities: Multiphase Design Study
Bibliographic record
Abstract
BACKGROUND: An increase in the demand for child participation in health care requires tools that enable and empower children to be involved in the co-production of their own care. The development of such tools should involve children, but participatory design and research with children have challenges, in particular, when involving children with disabilities where a low level of participation is the norm. Norm-creative and participatory approaches may bring more effective design solutions for this group. "Personas" is a methodology for increasing user perspectives in design and offers representation when users are absent. However, research on participatory persona generation in this context is limited. OBJECTIVE: The objective of this study was to investigate how norm-creative and participatory design approaches can be integrated in a persona generation method to suit children with disabilities in the design of games for health that target this group. METHODS: The method development involved interview transcripts and image-based workshops. Sixteen children with various disabilities participated in persona generation through co-creation of characters and scenarios. The results from the workshops were validated together with 8 children without disabilities, 1 young adult with a disability, and 1 rehabilitation professional. A qualitative thematic design analysis was iterated throughout the process. RESULTS: The results consisted of an image-based and iterative co-construction method. It was accompanied by examples of personas that were generated and validated within a games for health case. The method showed effectiveness in enabling flexible co-construction and communication. The data resonated with social model perspectives, and the development is discussed in terms of participation levels, salutogenic descriptions of barriers, and norm-creative tradeoffs. CONCLUSIONS: The resulting method may influence future design projects toward more inclusiveness and enable increased representation for children with disabilities in research and design. Using this method to its full potential requires a norm-critical awareness as well as extensive facilitation. Suggestions for further research include the application of the method to design processes in similar contexts or user groups.
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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 | 0.118 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".