A ResearchKit app to deliver paediatric electronic consent: Protocol of an observational study in adolescents with arthritis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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 it