Characteristics of Walk-In Clinic Physicians and Patients in Ontario, Canada: A Cross-Sectional Study
Bibliographic record
Abstract
ABSTRACT Objective We aimed to describe family physicians who primarily practice in a walk-in clinic setting and compare them to family physicians who provide longitudinal care. Design A cross-sectional study that linked results from an annual physician survey (2019) to administrative healthcare data from Ontario, Canada. We compared the characteristics, practice patterns, and patients of physicians primarily working in a walk-in clinic setting, with family physicians providing longitudinal care. Setting Ontario, Canada. Participants Physicians who primarily worked in a walk-in clinic setting in 2019, as indicated by an annual physician survey. Exposure Whether the physician was a walk-in clinic physician or a family physician who provided longitudinal care. Main Measures Physician demographic and practice characteristics, as well as their patients’ demographic and healthcare utilization characteristics. Results Compared to the 9,137 family physicians practicing longitudinal care, the 597 physicians who self-identified as practicing primarily in walk-in clinics were more frequently male (67% vs. 49%) and could speak a language other than English or French (43% vs. 32%). Walk-in clinic physicians had more encounters with patients who were younger ( M 37 vs. 47 years), had lower levels of prior healthcare utilization (15% vs. 19% in highest band), who resided in large urban areas (87% vs. 77%), and in highly ethnically diverse neighborhoods (45% vs. 35%). Walk-in clinic physicians had more encounters with unattached patients (32% vs. 17%) and with patients attached to another physician outside their group (54% vs. 18%). Conclusion Physicians who primarily work in walk-in clinics saw many patients from historically underserved groups, and many patients who were attached to another family physician.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".