Experiential interprofessional education for medical students at a regional medical campus
Bibliographic record
Abstract
BACKGROUND: Regional medical campuses are often challenged with providing effective interprofessional education (IPE) opportunities for medical students that are comparable to those at main campuses. At distributed teaching sites, there is often less IPE infrastructure and fewer learners of other health professions. On the other hand, distributed medical education (DME) settings often have community-based clinical environments and fewer medical students, which can provide unique opportunities for IPE curriculum innovation. METHODS: At the Niagara Regional Campus (NRC) of McMaster University, the Horizontal Elective for Interprofessional Growth & Healthcare Team ENhancement (HEIGHTEN) was developed to provide first-year medical students the opportunity to learn from and work alongside nurses in a community hospital. This study assesses HEIGHTEN's impact on students' knowledge, confidence, and attitudes towards interprofessional care, as well as student satisfaction with the learning experience using a mixed methods evaluation. RESULTS: Findings suggest that HEIGHTEN provided an enjoyable learning experience, fostered positive interprofessional attitudes and an appreciation for the nursing role. Voluntary participation by medical students was high and increased both within the regional campus and with students from other campuses travelling to participate. CONCLUSION: This model for IPE can be feasibly replicated by distributed teaching sites to provide medical students with hands-on, experiential learning early in training, leading to positive attitudes and behaviours supporting interprofessional collaboration (IPC).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".