Urgent Care Centres: A Scan of Models of Care and Literature on Their Effectiveness
Bibliographic record
Abstract
What is the Issue? Urgent care centres (UCCs) are medical clinics that provide same-day urgent treatment for non-life-threatening illnesses or injuries. The level of services they can provide is more comprehensive than primary care walk-in clinics but less comprehensive than emergency departments (EDs). In Canada, many people do not have timely access to primary care services, and EDs face overcrowding, which results in long wait times and patients leaving without being seen. UCCs may fill a gap between primary care and emergency services; however, there is no single definition or specified model for these clinics. There is a need for detailed information about the range and scope of UCCs operating across Canada. What Did We Do? We surveyed health care and administrative professionals about the characteristics and their experiences of UCCs in Canadian jurisdictions. We searched key resources, including journal citation databases, and conducted a focused internet search for relevant evidence published since 2015, to examine the effectiveness of UCCs in Canada or countries with similar health care systems. What Did We Find? We received 17 complete survey responses from 7 jurisdictions in Canada. Based on the responses, we found that UCCs vary in their structural and operational components. UCCs typically offer services such as diagnosis and treatment, X-rays, stitches, and lab tests. They are staffed by emergency and family physicians, nurses, allied health care professionals, and administrative staff. Some UCCs operate 24 hours a day, while others have limited hours. The most commonly identified barriers to operating UCCs include staffing and funding. Benefits of these centres were reported to include better access to care and potential relief of some of the burden on EDs. We identified 6 articles that met our inclusion criteria. Two articles examined UCCs dedicated to cancer-related concerns, and 4 articles examined general UCCs. The authors’ conclusions about the effectiveness of UCCs were mixed. What Does This Mean? The findings of this report can be used as guidance to policy- and decision-makers across Canada who may be in the process of, or considering, implementing UCCs. The findings provide examples of structural and operational components that may suit the needs and contexts of their respective jurisdictions as well as considerations that may help inform implementation.
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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 | 0.051 | 0.147 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.014 | 0.022 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.010 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.018 | 0.001 |
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".