Principle-driven virtual care practice to ensure quality and accessibility
Bibliographic record
Abstract
COVID-19 has accelerated the use of telehealth or virtual care (VC) as an alternative form of health care delivery. Clearly, VC provides unprecedented convenience and timeliness for patients seeking care from their health professionals. As a result, a substantial increase in telehealth providers is occurring, and the Canadian government is investing millions to support digital health care treatments and telehealth services. However, it is vital that the health professional community carefully examine the quality of care being delivered digitally and determine when it is appropriate to use VC as an alternative to face-to-face care. This article highlights some principles for health policymakers, health professionals, and health consumers to consider to ensure that VC is used appropriately and ethically for the right health conditions and in the right contexts.
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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 | 0.161 | 0.127 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.026 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.005 | 0.027 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.010 | 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".