The Regulatory Challenge of Mobile Health: Lessons for Canada
Bibliographic record
Abstract
Mobile health (mHealth) is the provision of health or medical services enabled by portable devices. This field is rapidly expanding as the global market for mobile devices grows. mHealth "apps" pose benefits and risks to their users that governments have attempted to address through regulation. There is substantial variability across regulatory bodies in the scope, specificity and robustness of mHealth regulations, with Canada's regulatory framework lacking in two major domains: (1) specificity of existing regulations for mHealth and (2) regulatory clarity for what apps require regulation. If Canada is to be a leader in digital health, it requires a new framework that encourages the growth of an mHealth market that can bring innovative solutions to contemporary healthcare challenges while maximizing user benefits and minimizing harms.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".