Models in interprofessional education: The IP enhancement approach as effective alternative
Bibliographic record
Abstract
OBJECTIVE: This article discusses the strategies and challenges of implementing interprofessional education interventions with students from different disciplines. It reviews two models of interprofessional education in academic prelicensure curricula including the extra-curricular and the crossbar models by considering ease of implementation, program reach and sustainability. It also introduces the interprofessional enhancement approach as an additional curriculum development strategy. RESULTS: The Alberta Interprofessional Education for Collaborative Patient-Centred Practice project used the Interprofessional Enhancement Approach by integrating course content into existing placement courses for nursing, respiratory therapy, pharmacy, and physiotherapy students. The students conducted their regular discipline-specific placements at various clinical sites in southern Alberta, Canada that were supplemented by three interprofessional strategies: mentoring, workshops and online discussions. The intervention reached over sixty individuals including students, preceptors and faculty. CONCLUSION: As compared to other approaches (extra-curricular and crossbar models), this approach shows that IP course content can be added to placement courses without restructuring complete curricula. This article intends to initiate further discussions about different IP education models in prelicensure education.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.007 |
| 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".