Regulating mobile health research in Canada: Public trust and public participation
Bibliographic record
Abstract
Smartphone applications provide unique opportunities for health research. Prospective participants may be recruited, give consent, and may share personal health information with researchers with unparalleled efficiency. Mobile health apps thus have the potential to profoundly alter the way health research is conducted, thereby contributing to more effective and more equitably distributed clinical interventions. But the regulation of this kind of research is uncertain. The absence of regulatory guidance may limit mobile health's promise. The mobile health research landscape in Canada is considered in the context of the existing regulatory framework, academic literature, and current applications. This paper outlines regulatory issues in the Canadian context, suggesting three key issues to which researchers and regulators ought to be attentive to ensure public trust: consent, return of results, and privacy and security. Further consideration of regulatory and ethical issues is needed if mobile health will earn the public's trust and promote public participation.
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.
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 | 0.104 | 0.179 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.030 | 0.036 |
| Scholarly communication | 0.022 | 0.007 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.011 | 0.016 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".