Mind the App: Considerations for the Future of Mobile Health in Canada (Preprint)
Bibliographic record
Abstract
UNSTRUCTURED Over the past decade, smartphone technology has become increasingly sophisticated and ubiquitous. Modern smartphones, now owned by more than three quarters of Canadians and 94% of millennials, perform an array of functions that are potentially useful in the health care context, such as tracking fitness data, enabling health record sharing, and providing user-friendly platforms for disease management. Approximately half of smartphone users have downloaded at least one health app, and clinicians are increasingly using them in their practice. However, despite widespread use, there is little evidence that supports their safety and efficacy. Few apps have been independently evaluated and many lack basic patient protections such as privacy policies. In this context, the demand for the regulation of mobile health apps has increased. Against this backdrop, regulators, including Health Canada, have begun to propose regulating the use of smartphones in health care. In this viewpoint, we respond to Health Canada’s recent proposal to regulate smartphone use in Canada according to a risk-based model. We argue that although Health Canada’s recent proposed approach is promising, it may require complementary regulation and oversight.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.022 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.013 | 0.006 |
| Insufficient payload (model declined to judge) | 0.061 | 0.017 |
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".