The app will see you now: mobile health, diagnosis, and the practice of medicine in Quebec and Ontario
Bibliographic record
Abstract
Mobile health applications are increasingly being used as tools of medicine. Outside of the clinic, some of these applications may contribute to diagnoses made absent a physician's care. We argue that this contravenes reservations of diagnosis to healthcare professionals in the law of two Canadian provinces: Quebec and Ontario. On the one hand, the law conceives of diagnosis in relatively broad terms. Drawing an association between symptoms and illness, for example, has been recognized in case law as sufficient. On the other hand, provincial law reserves diagnosis to physicians and other healthcare professionals. We argue that a number of health applications are capable of drawing associations between symptoms and disease and, in doing so, of delivering diagnoses in contravention of the law of Quebec and Ontario. This places mobile health applications in a poorly understood legal space. While prosecution is unlikely, the increasing ubiquity and technological sophistication of health applications promises to make such diagnosis widespread. We suggest that the legal status of such mobile health apps should be given serious attention. While our analysis focuses on the state of the law in Canada's largest provinces, we suggest that our argument will have implications in other jurisdictions.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.019 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".