Assessing Student Attitudes as a Result of Participating in an Interprofessional Healthcare Elective Associated with a Student-Run Free Clinic
Bibliographic record
Abstract
BACKGROUND: An interprofessional elective using a student-run clinic can introduce students to professional roles, collaborative patient care, and health disparities. METHODS AND FINDINGS: Students from four professions (pharmacy, medicine, physician assistant, and physical therapy) participated in a service-learning elective where they received weekly didactic lectures and provided healthcare in a student-run free clinic. Additional interprofessional activities included a quality improvement project and a case presentation. Students were administered anonymous surveys before and after the elective to assess changes in their attitudes toward interprofessional teamwork. A total of 93 and 74 students completed the pre-survey and post-survey, respectively. After participating in the elective, significantly more students reported working in interprofessional teams and understood the role of physician assistants. The majority of other attitudes about interprofessional collaboration and professional roles were sustained or improved after the elective. CONCLUSION: An interprofessional service-learning elective using didactic and experiential learning in an interprofessional, student-run free clinic sustained or improved student attitudes toward interprofessional teamwork. The elective had a significant impact on increased student experience working in interprofessional healthcare teams and increased understanding of health professions' roles. Continued assessment of the impact on student behaviours and patient outcomes is warranted.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".