Who provides walk-in services? Survey of primary care practice in Ontario.
Bibliographic record
Abstract
OBJECTIVE: To compare walk-in clinics with other primary care settings on characteristics associated with best practices in primary care. DESIGN: A mailed self-administered questionnaire asked about organizational and clinical characteristics of primary care practices located in major urban and suburban areas in Ontario. SETTING: Four types of fee-for-service group practices: walk-in and urgent-care clinics (WICs), mixed practices (MPs), after-hours clinics (AHCs), and group family practices (GFPs). PARTICIPANTS: A physician or a staff member involved in practice administration. MAIN OUTCOME MEASURES: The four practice types were compared on organizational characteristics and measures of access, continuing care, comprehensiveness, coordination, and mechanisms for monitoring quality of care. RESULTS: Walk-in clinics, MPs, and AHCs were open more hours during evenings and weekends and were more likely to see patients without appointments; GFPs were more likely to have on-call arrangements. Group family practices saw a larger proportion of patients for whom they provided ongoing care; WICs and MPs reported that more than 60% of their visits were with "regular" patients. Walk-in clinics were less likely to provide preventive services and psychological counseling than were GFPs and MPs. A few WICs, MPs, and GFPs had procedures to support coordination of care or to monitor quality of care. CONCLUSION: Although WICs, MPs, and AHCs provided walk-in services to Ontario patients, WICs and MPs also provided a substantial amount of ongoing care and preventive services. Independent AHCs appeared to most closely fit the "walk-in clinic" stereotype.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".