Designing eHealth Interventions for Pediatric Emergency Departments: Protocol for a Usability Testing Study With Youth, Parent, and Clinician Participants
Bibliographic record
Abstract
BACKGROUND: Usability tests provide important insight into user preferences, functional issues, and differences between target groups for health interventions and products. However, there is limited guidance on how to adapt the usability testing approach for a youth audience, especially for digital health interventions. OBJECTIVE: This protocol paper outlines a novel approach for conducting usability tests with a diverse audience of youth, parents, and clinicians in the development of 2 digital health tools for the pediatric emergency department (ED) setting. METHODS: This paper outlines a protocol for usability testing as part of a broader study aimed at co-designing ED discharge communication tools with youth, parents, and clinicians. The broader study involved co-designing 2 digital tools: one for asthma and one for concussions. A multimethods approach to usability testing was used to assess the functionality of these tools through 2 rounds of testing. A mix of youth, parents, and ED clinicians were invited to participate in each round of usability testing. Participants were asked to provide feedback on the tools through quantitative surveys and open-ended qualitative questions. The usability testing approach was adapted to suit each target group, such as including a youth in the data collection process, to enhance the quality of the data. The severity of usability problems was analyzed following the first round of testing, and each tool was refined based on this feedback. The second round of usability tests involved collecting both qualitative and quantitative feedback on the revised tools. RESULTS: All usability data have been collected and are being analyzed. Outcomes will be disseminated through a subsequent publication. Results will include demographic characteristics from each user group from both rounds of testing, severity of usability scores, qualitative and quantitative feedback, and differences in test outcomes between each target group. CONCLUSIONS: This paper provides novel guidance for conducting usability tests with youth participants when designing digital health tools. By using a comprehensive co-design and usability testing approach, we anticipate that final tools will be highly relevant to the end users and will lead to better uptake and patient outcomes when pilot-tested in future studies. The outlined approach may be adapted to different health care contexts for other youth participants. Further research should continue to explore ways to design usability tests that are suitable for youth audiences, as there is still a significant gap in the literature around this topic. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/64350.
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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 | 0.086 | 0.077 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.043 | 0.011 |
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".