Protecting the public interest while regulating health professionals providing virtual care: A scoping review
Bibliographic record
Abstract
Technology is transforming service delivery in many health professions, particularly with the rapid shift to virtual care during the COVID-19 pandemic. Health profession regulators must navigate legal and ethical complexities to facilitate virtual care while upholding their mandate to protect the public interest. The objectives of this scoping review were to examine how the public interest is protected when regulating health professionals who provide virtual care, discuss policy and practice implications of virtual care, and make recommendations for future research. We searched six multidisciplinary databases for academic literature published in English between January 2015 and May 2021. We also searched specific databases and websites for relevant grey literature. After screening, 59 academic articles and 18 grey literature sources were included for analysis. We identified five key findings: the public interest when regulating health professionals providing virtual care was only implicitly considered in most of the literature; when the public interest was discussed, the dimension of access was emphasized; criticism in the literature focused on social ideologies driving regulation that may inhibit more widespread use of virtual care; subnational licensure was viewed as a barrier; and the demand for virtual care during COVID-19 catalyzed licensure and scope of practice changes. Overall, virtual care introduces new areas of risk, potential harm, and inequity that health profession regulators need to address as technology continues to evolve. Regulators have an essential role in providing clear standards and guidelines around virtual care, including what is required for competent practice. There are indications that the public interest concept is evolving in relation to virtual care as regulators continue to balance public safety, equitable access to services, and economic competitiveness.
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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 | 0.040 | 0.167 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".