A survey of Canadian interprofessional student-run free clinics
Bibliographic record
Abstract
Student-run free clinics (SRFCs) have existed in Canada since 1971, providing interprofessional healthcare to underserved populations. SRFCs are seen as vehicles for socially accountable health professional education. Literature on how Canadian SRFC function is lacking. Web-based surveys were sent to student leaders from Canadian SRFCs regarding their 2014 activities. All six fully-functioning SRFCs responded reporting on the following: services provided, professions involved, governing structure, funding sources, clients seen, types of care sought, students and preceptors involved, as well as perceived strengths, weaknesses, opportunities and threats. In 2014, 2,159 clients were provided clinical care at Canadian SRFCs. The most common reasons for visiting included pain and infection. Strengths identified include autonomy, ability to adapt to client needs, serving the underserved, and real-world interprofessional teamwork. Weaknesses reported include high student and preceptor turnover. Threats include securing funding and liability coverage. Since there is little literature on Canadian SRFCs, we compared our results with United States (US) based SRFCs. Canadian SRFCs share core values with US-based SRFCs and report similar strengths and challenges. However, Canadian SRFCs differ in scope and appear to provide care for more acute concerns. Data from studies of US-based SRFCs may not be immediately applicable to Canadian SRFCs. Studies evaluating Canadian SRFCs are needed.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".