Walk-in clinic patient characteristics and utilization patterns in Ontario, Canada: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Walk-in clinics are common in North America and are designed to provide acute episodic care without an appointment. We sought to describe a sample of walk-in clinic patients in Ontario, Canada, which is a setting with high levels of primary care attachment. METHODS: We performed a cross-sectional study using health administrative data from 2019. We compared the sociodemographic characteristics and health care utilization patterns of patients attending 1 of 72 walk-in clinics with those of the general Ontario population. We examined the subset of patients who were enrolled with a family physician and compared walk-in clinic visits to family physician visits. RESULTS: Our study found that 562 781 patients made 1 148 151 visits to the included walk-in clinics. Most (70%) patients who attended a walk-in clinic had an enrolling family physician. Walk-in clinic patients were younger (mean age 36 yr v. 41 yr, standardized mean difference [SMD] 0.24), yet had greater health care utilization (moderate and high use group 74% v. 65%, SMD 0.20) than the general Ontario population. Among enrolled Ontarians, walk-in patients had more comorbidities (moderate and high count 50% v. 45%, SMD 0.10), lived farther from their enrolling physician (median 8 km v. 6 km, SMD 0.21) and saw their enrolling physician less in the previous year (any visit 67% v. 80%, SMD 0.30). Walk-in encounters happened more often after hours (16% v. 9%, SMD 0.20) and on weekends (18% v. 5%, SMD 0.45). Walk-in clinics were more often within 3 km of patients' homes than enrolling physicians' offices (0 to < 3 km: 32% v. 22%, SMD 0.21). INTERPRETATION: Our findings suggest that proximity of walk-in clinics and after-hours access may be contributing to walk-in clinic use among patients enrolled with a family physician. These findings have implications for policy development to improve the integration of walk-in clinics and longitudinal 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".