Early adoption of virtual care in primary care settings in manitoba, canada
Bibliographic record
Abstract
Context: Prior to the COVID-19 pandemic, there was limited integration of virtual care (VC) in primary care clinical practice. However, the pandemic precipitated a shift with primary care providers utilizing VC to facilitate safe access to primary health services. Objective: To describe and characterize VC visits by primary care providers to patients following the introduction of the VC tariff code in Manitoba, Canada. Design: Retrospective cohort study. Setting: Primary care clinics that offered at least one VC visit and participate in the Manitoba Primary Care Research Network (MaPCReN), a practice-based network that contains de-identified EMR data from 265 primary care clinicians in Manitoba. Population: All encounters with a primary care provider participating in MaPCReN between 01/01/18 and 06/30/20. Outcome Measures: Tariff codes from billing records between 03/14/20 and 06/30/20 determine the visit type (clinic visit, virtual visit). Patient (sex, age, comorbidities, visit frequency, medication rates) and provider (sex, age, clinic location, provider type, remuneration model, country of graduation, return visit rate) characteristics describe the study population based on visit type. Generalized estimate equation models describe factors associated with VC. Results: There were 154 primary care providers that provided on average, VC for 47.6% of their patient visits. Among the 142,616 patients, 19.4% had at least one virtual care appointment, 29.4% had only a clinic visit, and 51.2% did not attend a visit with their primary care provider. Female patients (OR 1.16, CI 1.09-1.22) with ≥3 comorbidities (OR1.71, CI 1.44-2.02), ≥10 medications (OR 2.71, CI 2.2-1.53) have significantly higher odds of VC than male patients, with no comorbidities and no prescriptions. Follow-up visits were required for 21.2% of VC encounters; the majority (55.9%) had the same visit type. For follow-up encounters where the visit type changed, 42.2% of clinic visits were followed by VC, whereas 26.9% of VC was followed by a clinic visit. Conclusion: During the first 3 months of pandemic restrictions, there was an increase in the use of VC in Manitoba’s primary care settings. VC was utilized more commonly by patients with the most comorbidities and prescriptions, suggesting that those who require ongoing attention from primary care made use of VC services.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".