Fantastic perspectives and where to find them: involving patients and citizens in digital health research
Bibliographic record
Abstract
BACKGROUND: Digital contact tracing and exposure notification apps have quickly emerged as a potential solution to achieve timely and effective contact tracing for the SARS-CoV-2 virus. Nonetheless, their actual uptake remains limited. Citizens, including patients, are rarely consulted and included in the design and implementation process. Their contribution supports the acceptability of such apps, by providing upstream evidence on incentives and potential barriers that are most relevant to users. The DIGICIT (DIGITal CITizenship) project relied on patient and citizen partnership in research to better integrate public perspectives on these apps. In this paper, we present the co-construction process that led to the survey instrument used in the DIGICIT project and the interpretation of its results. This approach promotes public participation in research on contact tracing and exposure notification apps, as well as related digital health applications. OBJECTIVES: This article has three objectives: (1) describe the methodological process to co-construct a questionnaire and interpret the survey results with patients and citizens, (2) assess their experiences regarding this methodology, and (3) propose best practices for their involvement in digital health research. METHODS: The DIGICIT project was developed in four steps: (1) creation of the advisory committee composed of patients and citizens, (2) co-construction of a questionnaire, (3) interpretation of survey results, and (4) assessment of the experience of committee participants. RESULTS: Of the 25 applications received for participation in the advisory committee, we selected 12 people based on pre-established diversity criteria. Participants initially generated 84 survey questions in the first co-construction meeting, and eventually selected 36 in the final version. Participants made more than 20 recommendations when interpreting survey results and suggested carrying out focus groups with marginalized populations to increase representativity. They appreciated their inclusion early in the research process, being listened to and respected, the collective intelligence, and the method used for integrating their suggestions. They suggested that the study objectives and roles be better defined, that more time in the brainstorming sessions be allowed, and that discussion outside of meetings be encouraged. CONCLUSION: Having patients and citizens actively participating in this research constitutes the main methodological strength. They enriched the study from start to finish, and recommended the addition of focus groups to seek the perspective of marginalized groups that are typically under-represented from digital health research. Clear communication of the project objectives, good organization in meetings, and continuous evaluation from participants allow best practices to be achieved for patients' and citizens' involvement in digital health research. Co-construction in research generates critical study design ideas through collective intelligence. This methodology can be used in various clinical contexts and different healthcare settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".