Interprofessional Collaboration Led by Health Professional Students: A Case Study of the Inter Health Professional Alliance at Virginia Commonwealth University
Bibliographic record
Abstract
Background: Internationally recognized health experts have identified the need for an interdisciplinary approach to meet the healthcare needs of the 21st century, but academic institutions have been slow to take action. In response, eight health professional students at Virginia Commonwealth University developed a student-led organization, the Inter Health Professionals Alliance (IHPA), to foster a collaborative, interdisciplinary environment among health professional students.Methods and Findings: The eight students utilized a participatory action research approach to identify 1) an understanding behind the motivation for developing IHPA and 2) the core benefits of group involvement. Four benefits were identified: the development of knowledge and skills, interprofessional networks, professional competence, and role clarity. The case study demonstrated that students can engage in interdisciplinary collaboration from a student-initiated approach and likely improve the care of future patients. Drawing on personal experiences, IHPA board members outline five pieces of wisdom to aid fellow students in the development of student-led interdisciplinary organizations.Conclusions: With enthusiasm and support, students can transform their educational experiences to meet the healthcare needs of the twenty-first century.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.033 | 0.009 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.007 | 0.009 |
| 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".