Use of virtual care by infectious disease specialists in Canada: A national survey
Bibliographic record
Abstract
Objective: The aim of this study was to characterize the type and extent of virtual care use among infectious disease specialists in Canada, with a focus on the clinical factors that influence the decision to provide virtual versus in-person care. Methods: Infectious disease physicians practicing in Canada were invited to complete a survey regarding their experiences with virtual care. The survey included 14 vignettes depicting new outpatient and post-hospital-discharge referrals. Participants were asked to select which (if any) virtual care modalities they would feel comfortable using and to specify a reason if they did not feel comfortable providing care virtually. Machine learning and natural language processing techniques were used to identify themes. Results: In total, 57 infectious disease physicians completed the survey. Respondents reported devoting 36.5% (SD, 18.4%) of their infectious disease practice to outpatient care, with 44.2% (SD, 23.2%) of it being delivered virtually. Respondents were more comfortable providing virtual care to post-hospital-discharge referrals who had been seen by an infectious disease physician compared to new outpatient referrals. When respondents were not comfortable with using any virtual care modality, the following common themes emerged: the need for physical examination, the importance of establishing a therapeutic relationship, the need for additional in-person tests or diagnostics, and patient counselling. Conclusion: This study provides a glimpse into the current state of virtual care use in Canada and some of the major themes that affect decision making for virtual versus in-person care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".