Redesigning primary care
Bibliographic record
Abstract
OBJECTIVE: To explore primary care physician (PCP) perspectives on the clinical utility of virtual visits. DESIGN: Qualitative design involving semistructured interviews. SETTING: Primary care practices within 5 regions in southern Ontario. PARTICIPANTS: Primary care physicians representing different practice sizes and remuneration models. METHODS: Interviews were conducted with PCPs who were involved in a large-scale pilot implementation of virtual visits (patient-provider asynchronous messaging, or synchronous audio or video communication). The first phase involved a convenience sample of users in the first 2 regions where the pilot was initiated; after implementation in all 5 regions, purposive sampling was used to ensure diversity within the sample (eg, physicians representing different use frequencies of virtual visits, regions, and remuneration models). Interviews were audiorecorded and transcribed. An inductive thematic analysis was used to identify prominent themes and subthemes. MAIN FINDINGS: Twenty-six physicians were interviewed. Fifteen were recruited using convenience sampling and 11 through purposive sampling. Four themes regarding the clinical utility of virtual visits were identified: virtual visits can effectively resolve many patient concerns, with some variation in PCP comfort using virtual visits for specific conditions; virtual visits are beneficial for a range of patients but some patients might overuse or inappropriately use them; PCPs prefer to use asynchronous messaging (eg, text or online messaging) because of its convenience and flexibility; and virtual visits can provide value at the patient, provider, and health system levels. CONCLUSION: While participants believed that virtual visits can be appropriately used to resolve a variety of clinical concerns, they found in practice that virtual visits are fundamentally different from face-to-face encounters. Professional guidelines on appropriate use cases should be established to develop a standard framework for virtual 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 | 0.021 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".