Student and Preceptor Experiences at an Inter-Professional Student-Run Clinic: A Physical Therapy Perspective
Bibliographic record
Abstract
Purpose: Student-run clinics (SRCs) provide a unique opportunity for inter-professional education; they prepare health care students for a collaborative future by enabling them to interact with other such students in a clinical setting focused on inter-professional learning and collaboration. Physical therapy (PT) students are increasingly being included in SRCs; however, most research on student experiences in SRCs has been carried out with medical students. This qualitative study explores the perceived benefits of the PT experience in an SRC through the lens of PT students and their preceptors. Method: A qualitative interpretive–descriptive approach consisting of face-to-face, semi-structured interviews was used. Interviews were audiotaped and transcribed verbatim. Data were analyzed using a constant comparative approach. Results: Seven PT students and eight preceptors who volunteered at the SRC between September 2013 and May 2015 participated in the study. Three themes emerged from the interviews: (1) exposure to marginalized patient populations, (2) learning through inter-professional interactions, and (3) experience with different patient care approaches. Conclusions: Participating in an SRC enhances PT students' understanding of their and other health care professionals' roles. Students gained an appreciation for the social determinants of health and improved their knowledge of inter-professional collaboration. The knowledge gained from this study has the potential to inform PT professional development, SRCs, and PT education.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".