Student leadership in interprofessional education: benefits, challenges and implications for educators, researchers and policymakers
Bibliographic record
Abstract
Context Interprofessional collaboration is gaining increasing prominence as a team-based approach to health care delivery that synergistically maximises the strengths of each health professional to enhance patient care, decrease medical errors and optimise efficiency. The often neglected role that student leaders have in preparing their peers, as the health professionals of the future, for collaboration in health care should not be overlooked. Objective This paper offers the foundational arguments supporting the integral role that student leadership in interprofessional education (IPE) can play and its comparative advantages. Methods Evidence from previous literature and the National Health Science Students' Association in Canada was reviewed and a questionnaire on student-initiated IPE was administered among Canada's top student leaders in this area. Results Student leadership is essential to the success of IPE because it enhances students' willingness to collaborate and facilitates the longterm sustainability of IPE efforts. Student-initiated IPE, a subset of student leadership, is particularly important to achieving the aforementioned goals and offers a number of benefits, comparative advantages and associated challenges. Conclusions Successful student leadership in IPE will yield significant benefits for everyone in the years to come. However, it requires the support of educators, researchers and policymakers in fostering an enabling environment that will facilitate the efforts and contributions of student leaders.
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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 | 0.029 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 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".