Can I Recommend this App? mHealth Recommendation Guidelines by Health Professional Governing Bodies in Canada
Bibliographic record
Abstract
Mobile health (mHealth) applications have gained considerable popularity and have crossed into clinical practice as well. Users are turning to healthcare professionals for recommendations on mHealth solutions to help them achieve their desired health outcomes. However, healthcare professionals may not be in a position to answer this question, and their professional governing bodies have provided limited actionable guidance on this subject as well. We explore this issue by scanning Canadian health professional governing bodies for guidance on recommending mHealth solutions to patients. Initial results suggest there are five broadly defined forms of guidance: mention of mHealth, not to recommend, preapproving, criteria, and index. Furthermore, we argue that the information systems discipline is well-positioned to contribute to this area. We draw on parallels between information systems design principles and clinical guidelines to suggest how design science research can support the development of guidance for healthcare professionals on mHealth app recommendations.
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 | 0.039 | 0.133 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".