Designed for simplicity, used for complexity: The systemic pressures shaping walk-in clinic practices and outcomes
Bibliographic record
Abstract
Walk-in clinics (WICs), appreciated for their accessibility and convenience, have become an increasingly popular healthcare option in Ontario for patients with and without primary care enrolment. Despite their utility, WICs face criticism for delivering lower-quality care compared to comprehensive, enrolment-based primary care models. Critics argue that WICs contribute to system inefficiencies and encourage practice patterns misaligned with population health goals. This study explored physician perspectives on two key outcomes often associated with low-quality care in WICs: repeat primary care visits and potentially inappropriate antibiotic prescribing. Using a qualitative descriptive approach, semi-structured interviews were conducted with Ontario-based family physicians (N = 19) who had experience practicing in both WICs and enrolment-based primary care. The findings highlight systemic challenges, including limited access to enrolment-based primary care and increasing healthcare demands, which have pushed WICs beyond their intended role. This misalignment has created tensions between their structure and purpose, resulting in visits that participants described as more transactional than those in primary care. These constraints-rooted in a lack of informational and relational continuity-often limited participants' ability to provide in-depth engagement or follow-up care. Repeat visits were frequently linked to efforts to ensure continuity for complex or chronic conditions. Similarly, participants acknowledged the reality of potentially inappropriate antibiotic prescribing, attributing it to the high patient volume, desire to satisfy patient expectations, and a tendency to "err on the side of caution" when the nature of the illness is in question. The findings underscore how health system pressures and well-intended policies, such as Ontario's primary care access bonus, can produce unintended consequences, including inequities in access and difficulties with care coordination across settings. Addressing these challenges requires reforms to better integrate WICs with the primary care system, alongside tailored training to support physician decision-making in episodic care contexts.
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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 | 0.024 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".