What attracts students to interprofessional education and other health care reform initiatives?
Bibliographic record
Abstract
BACKGROUND: An international consensus has emerged that interprofessional education (IPE) and other health care reforms are necessary to address the increasing complexity of patients' health needs. Despite overwhelming barriers to its system-wide implementation, health professional students worldwide have organized themselves to promote IPE and have achieved considerable attention. This study seeks to offer insights into what attracts students to IPE and other health care reform initiatives and how advocates of change can stimulate this interest. METHODS: Using a qualitative research methodology, 69 students representing 25 disciplines from 22 institutions across North America were interviewed and surveyed on why and how they became interested in IPE. RESULTS: Students were attracted to the possibility of enhancing patient care (n=17), advancing their careers (n=17) and learning more about the issue (n=15). The participating students first became involved in IPE after they joined a student organization (n=21), attended an IPE conference (n=10) or received personal encouragement to do so from a dean (n=2), instructor (n=3), school administrator (n=7) or peer (n=11). These findings point to several strategies that advocates can use to capitalize on the potential of student advocacy to gain support for IPE and new health care innovations. CONCLUSION: This study is the first of its kind to delineate how clinicians, educators, researchers and policymakers can attract students to health care reform initiatives. This work can inform the strategic efforts of advocates to make the idea of IPE and health care reform more attractive to students (as both learners and leaders) and enlist their help in achieving it in the future.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".