Implementing virtual primary care: experiences, perspectives and identification of improvement opportunities in an academic primary care setting
Bibliographic record
Abstract
BACKGROUND: One of the biggest changes to primary care triggered by the COVID-19 pandemic was the rapid integration of virtual care (VC). VC offers benefits to patients and providers but implementation presents challenges. METHODS: This study is a secondary analysis of a 2021 quality improvement (QI) driven environmental scan comprising a survey and 1:1 interviews, at the Department of Family and Community Medicine at the University of Toronto. The scan aimed to understand the current and desired future use of VC at the 14 sites. RESULTS: The survey was completed by all sites between July and October 2021 and 1:1 interviews were conducted between October and November 2021 with 12 of the 14 site/QI leads. VC was seen as convenient and flexible, and as enabling continuity of care for patients who could not easily attend in-person. Factors enabling implementation of VC included leadership at both the system and local level; a shared understanding of VC on the part of providers, patients and clinical staff; and technological and administrative readiness. Challenges included the need for triage algorithms; incongruent expectations of VC by patients and providers; technology issues; increased administrative burden; and impacts on medical education. All anticipated that some degree of VC would continue in future. CONCLUSIONS: VC offered benefits but it also impacted clinical routines and administrative processes creating new forms of work for clinicians and staff. Patient education is needed to ensure that their expectations of VC align with those of providers. Research and QI efforts are required to optimise the use of VC in 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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".