Translating complex clinical environments to virtual care: Standards of Care in Virtual Medicine in Canada
Bibliographic record
Abstract
Context Rapid adoption of virtual care in early 2020, meant there was little time to develop or plan integrated virtual health care services, resulting in an urgency to establish governance for quality-based virtual care in Canada. Improvements in implementation of virtual care are needed across Canada for safer, equitable and accessible healthcare. Objective To identify existing regulatory frameworks and evaluate standards for virtual health care across Canada. Study Design and Analysis This is a comparative study, analyzing the variability of virtual care standards. Setting or Dataset Virtual care standards available on the website of the 13 Canadian provincial/territorial College of Physicians. Population Studied The 13 provinces and territories in Canada that are regulated by their relative College of Physicians. Intervention/Instrument Published Virtual care standards were searched on each College of Physicians website and Google. Only the most recent published standards were used in analysis. Outcome Measures Definition of Virtual Medicine, Ethical, Professional and Legal Obligations, Requirements that preceded Engaging in Virtual Medicine, Establishing a Patient-Physician Relationship, During and After Engaging in Virtual Medicine, Prescribing and Authorizing. Results Regulatory bodies defined virtual care similarly, however differences were noted in the inclusion of synchronicity and interprofessional consultation. Ethical, professional and legal requirements were alike for virtual care and in-person settings. Some governing bodies require prior licensure for “out-of-province” physicians to provide telemedicine within their jurisdictions. When establishing a Patient-Physician relationship the disclosure of the patient9s location, identity and verification of a safe physical setting was not mandatory across all jurisdictions. Not all regulatory authorities authorize virtual prescribing of controlled substances. Conclusions Standards for virtual health care are generally consistent with those established for inperson settings; however there remain serious gaps between regulatory bodies. These gaps and the lack of implementation planning render standardized training and learner competency development in virtual care management difficult. A pan-Canadian standard would diminish variability and allow for the development of standardized physician training to improve implementation of virtual care.
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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 | 0.030 | 0.114 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.014 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.001 | 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".