The Calgary student run clinic in context: a mixed-methods case study
Bibliographic record
Abstract
BACKGROUND: Student Run Clinics (SRCs) provide students with clinical education while caring for underserved populations. While much of the research on SRCs comes from the USA, SRCs in other contexts need to be appraised in the context of the systems they interact with. This study explored how stakeholders in the University of Calgary's SRC perceived its purpose and beneficiaries with respect to patients, students, undergraduate medical education, and its intersections within the healthcare system in Calgary. METHODS: Data came from the SRC's EMR and stakeholder interviews at the Inn from the Cold (IFTC) shelter. Qualitative data were analyzed using standard grounded theory techniques. RESULTS: There were 13 interviews - seven with student clinicians and six with preceptors and other stakeholders. Interviews highlighted the uncertainty of the SRCs role. Majority of participants saw the SRC as facilitating further access to other healthcare services, while some commented on its primarily education-focused role. Major limitations in the SRC's scope of care and its integration with other services were identified. CONCLUSION: SRCs need to consider their accountabilities, both educational and healthcare-focused at individual and organization levels, in order to function as responsible healthcare providers in Calgary.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| 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".