Health Care Utilization After a Visit to a Within-Group Family Physician vs a Walk-In Clinic Physician
Bibliographic record
Abstract
PURPOSE: Primary care access is a key health system metric, but little research has compared models to provide primary care access when one's regular physician is not available. We compared health system use after a visit with a patient's own family physician group (ie, within-group physician who was not the patient's primary physician) vs a visit with a walk-in clinic physician who was not part of the patient's family physician group. METHODS: We conducted a population-based, retrospective cohort study using administrative data from Ontario, Canada, including all individuals formally enrolled with a family physician, from April 1, 2019 to March 31, 2020. We compared those visiting within-group physicians to those visiting walk-in clinic physicians using propensity score matching to account for differences in patient characteristics. The primary outcome was any emergency department visit within 7 days of the initial visit. RESULTS: Matched patients who visited a within-group physician (N = 506,033) were 10% less likely to visit an emergency department in the 7 days after the initial visit compared to patients who saw a walk-in clinic physician (N = 506,033; 20,117 [4.0%] vs 22,320 [4.4%]; risk difference [RD] 0.4%; 95% CI 0.4-0.5; relative risk [RR] 0.90; 95% CI, 0.89-0.92). Restricting to visits occurring on weekends, the observed association was stronger (7,964 [3.7%] vs 10,055 [4.7%]; RD 1.0%; 95% CI 0.9-1.1; RR 0.79; 95% CI, 0.77-0.82). Those accessing after-hours within-group physician visits were more likely to have ≥1 additional virtual or in-person within-group physician visit within 7 days (virtual RR 1.86, in-person RR 1.87). CONCLUSIONS: Compared to visiting a walk-in clinic physician, seeing a within-group physician after hours might decrease downstream emergency department visits. This finding could be explained by better continuity of care and can inform primary care service models and the policies that support them.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".