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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".