Interprofessional Experiences at a Student-run Clinic: Who Participates and What Do They Learn?
Bibliographic record
Abstract
Background: Student-run Clinics (SRCs) are student-driven, interprofessional community service-learning primary care initiatives in which students of different disciplines work collaboratively under the supervision of licensed healthcare professionals. Despite their increasing prominence and promise as vehicles for interprofessional education, little is known about the characteristics of students or mentors who participate in these initiatives. Methods: A quality improvement review was conducted by members of an interprofessional, student-run clinic based on data collected in the first 3 years of clinic operation. Program records and anonymous feedback forms were examined for information regarding student and mentor characteristics (e.g., discipline, frequency of participation) and information regarding service provider satisfaction and recommendations. Findings: The STAR Clinic had low student retention with the majority of students attending only one clinic shift. There was also limited student and mentor diversity, with medicine and nursing most highly represented. Qualitative information highlighted areas of strength and opportunities for improvement. Conclusions: Recruitment and retention of students and mentors should be a priority for SRCs. Efforts devoted to increasing interprofessional diversity would likely benefit clients and allow for a more holistic approach to person-centred care.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".