Bibliographic record
Abstract
The adage "Never let a good crisis go to waste," widely attributed to Winston Churchill (Gruère 2019), has echoed throughout the COVID-19 pandemic.It aptly describes the rapid uptake of virtual care since March 2020 and other developments that it has inspired, including renewed attention to health information and data governance, interoperability, health equity, appropriateness and cross-border licensure.It was a privilege to be asked to be guest editor for this issue.My interest in virtual care comes from supporting the work of the Virtual Care Task Force (VCTF) that was convened by the Canadian Medical Association (CMA), College of Family Physicians of Canada (CFPC) and Royal College of Physicians and Surgeons of Canada (RCPSC) in March 2019.The VCTF issued its first report on February 11, 2020, just weeks before the COVID-19 pandemic was declared (CMA, CFPC and RCPSC 2020).Will Falk did a panoramic stock taking in early 2021 for Health Canada (Falk 2021), and I thank him for agreeing to revisit it for the lead paper for this issue and also thank the commentators for their thoughtful perspectives. Emerging Themes: Teamwork and AccessSome interesting themes have emerged from the commentaries that highlight important issues yet to be addressed.Teri Price (2022) underscores the potential for virtual care to support team-based care.She clearly defines what we mean by teamwork and sets out a scenario of what it could look like.She credits the leadership of Ewan Affleck on the Alberta Virtual Care Working Group (AVCWG), which included representatives from nursing, pharmacy, medicine and Alberta's regulated health professions as well as patients (AVCWG 2021).Erik Sande (2022) describes the success of mobile integrated health programs during the pandemic.Affleck (2022) notes the preponderance of the word "physician" in Will Falk's paper (Falk 2022).Bibliometric analysis would reveal whether this reflects the literature, but, in any case, more work is needed on how virtual care can enable team-based care.There are some papers that address this (Mitzel et al. 2021; Sinsky et al. 2021), and the development of guidance and tools would be helpful.One barrier to the adoption of team-based virtual care is the continued narrow interpretation of "insured services" by provincial and territorial Never Let a Crisis Go to Waste
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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 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.064 | 0.069 |
| Insufficient payload (model declined to judge) | 0.016 | 0.014 |
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".