Impact on Clinical Performance of Required Participation in a Student-Run Pro Bono Clinic
Bibliographic record
Abstract
Background and Purpose. Early opportunities for students to develop clinical skills and professional attributes are important to maximize clinical learning. Student-run pro bono clinics have the potential to provide early contextual exposure. This article describes the impact of required compared to voluntary participation in a student-run pro bono clinic on clinical performance during the first full-time internship. Method/Model Description and Evaluation. Students in the University of Florida Doctor of Physical Therapy program were assigned to one of four service learning groups including a pro bono clinic. While attendance at the clinic was encouraged for all students, only the assigned group was required to attend twice a semester. A retrospective analysis of student performance on the Clinical Performance Instrument (CPI) for the first internship was conducted. Clinician CPI ratings were categorized as beginner or intermediate and above. Median scores for safety, professional practice, and practice management items for those students who were required to attend the clinic were compared to students from the other service learning groups who did not attend or attended voluntarily using Chi-square analysis. Outcomes. At midterm, a higher proportion of the required pro bono group were rated as intermediate or above by clinical instructors for safety, all professional practice items except professional development, and all patient management items except diagnosis/prognosis, education, and consideration of financial resources. Differences were present at the final evaluation for safety, professional behavior, cultural competence, clinical reasoning, and examination. Discussion and Conclusion. Positive outcomes were present for students involved in the pro bono clinic, illustrating the potential benefits of required pro bono clinic experiences early in a professional curriculum for accelerating clinical performance.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".