A ResearchKit app to deliver paediatric electronic consent: Protocol of an observational study in adolescents with arthritis
Bibliographic record
Abstract
ResearchKit is an open-source software framework designed to streamline the process of screening and consenting participants into research studies. By digitizing traditionally analog processes, ResearchKit has potential to increase the reach, efficiency, and scalability of mobile health (mHealth) research. The model has been successfully applied in adult settings. However, to our knowledge, no group has sought to adapt ResearchKit for a paediatric research environment in Canada. The potential benefits for building paediatric mHealth apps compatible with remote eConsent are numerous: (1) access to studies can be broadened from small groups of children and families who live in close proximity to research sites to whole populations across geographical boundaries, (2) increased convenience for study participants because they can complete consent on their smartphone from their home, rather than in person or on paper, and (3) large-scale study enrollment can be conducted with fewer resources than traditional face-to-face methods. We describe the rationale and design of a proof-of-concept observational study focused on implementing remote eConsent in a Canadian paediatric population. A community-based sample of adolescents with arthritis will be remotely onboarded to use the iCanCope pain self-management app for 8-weeks. Outcomes will focus on: (1) fidelity and acceptability of the eConsent process, (2) fidelity of the iCanCope app in terms of engagement and acceptability, (3) participant study experience including level of perceived support and acceptability of study tasks, and (4) clinical outcomes related to use of the iCanCope app over an 8-week period.
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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 | 0.069 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.040 | 0.012 |
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".