Best Practices for Virtual Care: A Consensus Statement From the Canadian Rheumatology Association
Bibliographic record
Abstract
OBJECTIVE: To develop best practice statements for the provision of virtual care in adult and pediatric rheumatology for the Canadian Rheumatology Association's (CRA) Telehealth Working Group (TWG). METHODS: Four members of the TWG representing adult, pediatric, university-based, and community rheumatology practices defined the scope of the project. A rapid literature review of existing systematic reviews, policy documents, and published literature and abstracts on the topic was conducted between April and May 2021. The review informed a candidate set of 7 statements and a supporting document. The statements were submitted to a 3-round (R) modified Delphi process with 22 panelists recruited through the CRA and patient advocacy organizations. Panelists rated the importance and feasibility of the statements on a Likert scale of 1-9. Statements with final median ratings between 7-9 with no disagreement were retained in the final set. RESULTS: Twenty-one (95%) panelists participated in R1, 15 (71%) in R2, and 18 (82%) in R3. All but 1 statement met inclusion criteria during R1. Revisions were made to 5/7 statements following R2 and an additional statement was added. All statements met inclusion criteria following R3. The statements addressed the following themes in the provision of virtual care: adherence to existing standards and regulations, appropriateness, consent, physical examination, patient-reported outcomes, use in addition to in-person visits, and complex comanagement of disease. CONCLUSION: The best practice statements represent a starting point for advancing virtual care in rheumatology. Future educational efforts to help implement these best practices and research to address identified knowledge gaps are planned.
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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 | 0.220 | 0.177 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.013 | 0.014 |
| Research integrity | 0.015 | 0.020 |
| Insufficient payload (model declined to judge) | 0.002 | 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".