Characteristics and Health Care Use of Patients Attending Virtual Walk-in Clinics in Ontario, Canada: Cross-sectional Analysis
Bibliographic record
Abstract
BACKGROUND: Funding changes in response to the COVID-19 pandemic supported the growth of direct-to-consumer virtual walk-in clinics in several countries. Little is known about patients who attend virtual walk-in clinics or how these clinics contribute to care continuity and subsequent health care use. OBJECTIVE: The objective of the present study was to describe the characteristics and measure the health care use of patients who attended virtual walk-in clinics compared to the general population and a subset that received any virtual family physician visit. METHODS: This was a retrospective, cross-sectional study in Ontario, Canada. Patients who had received a family physician visit at 1 of 13 selected virtual walk-in clinics from April 1 to December 31, 2020, were compared to Ontario residents who had any virtual family physician visit. The main outcome was postvisit health care use. RESULTS: Virtual walk-in patients (n=132,168) had fewer comorbidities and lower previous health care use than Ontarians with any virtual family physician visit. Virtual walk-in patients were also less likely to have a subsequent in-person visit with the same physician (309/132,168, 0.2% vs 704,759/6,412,304, 11%; standardized mean difference [SMD] 0.48), more likely to have a subsequent virtual visit (40,030/132,168, 30.3% vs 1,403,778/6,412,304, 21.9%; SMD 0.19), and twice as likely to have an emergency department visit within 30 days (11,003/132,168, 8.3% vs 262,509/6,412,304, 4.1%; SMD 0.18), an effect that persisted after adjustment and across urban/rural resident groups. CONCLUSIONS: Compared to Ontarians attending any family physician virtual visit, virtual walk-in patients were less likely to have a subsequent in-person physician visit and were more likely to visit the emergency department. These findings will inform policy makers aiming to ensure the integration of virtual visits with longitudinal primary care.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".