What Is Possible If We Focus on Where Healthcare Is Going Instead of Where Medicine Has Been
Bibliographic record
Abstract
As Canadian leaders of the world's largest virtual care organization, we bring a national and a global perspective to our response to Falk's (2022) paper on virtual care in Canada in this issue. With more than 20 years of experience enabling virtual care and more than 90 million people accessing our virtual care services and tools in more than 170 countries, across more than 600 health systems and more than 70 clinical use cases, we have already done or witnessed first-hand many of the changes that Falk anticipates Canadians will contend with as we expand channels to and modalities of care beyond the incumbent monochannel of in-person, physician-mediated service delivery. In this essay, we respond to Falk's (2022) paper in three ways: (1) we disagree with the definition of virtual care; (2) we agree with - and expand on - the analysis and ideas; and (3) we reveal two gaps in Falk's analysis that will or should be at the forefront of the Canadian discourse. That is, we disagree with the narrow framing of virtual care, we agree with the locks and keys (and suggest, from experience, other ways to think about the keys) and we table important gaps that are notably missing from the debate.
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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 | 0.025 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.030 | 0.104 |
| Scholarly communication | 0.031 | 0.037 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.019 | 0.045 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".