Overcoming All Obstacles: A Framework for Embedding Interprofessional Education Into a Large, Multisite Bachelor of Science Nursing Program
Bibliographic record
Abstract
As the delivery of health care becomes more complex and challenging, the need for all health care professionals to collaborate as a team has been identified. Nurses are an integral part of the health care team, so it is critical that their education prepare them for interprofessional collaborative practice. Although many academic settings are currently offering interprofessional education (IPE) in the form of compulsory and elective activities and courses, it may not be enough nor an option for programs with large volumes of students who are distributed across a variety of sites and locations. This article outlines a framework that has been successfully adopted by one large school of nursing that chose to integrate interprofessional competencies throughout its curriculum. This IPE agenda is cost-effective, sustainable, and accessible, and it can be adapted to meet the needs of other prelicensure programs that face similar obstacles or challenges with offering IPE.
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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 | 0.027 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.016 | 0.021 |
| Scholarly communication | 0.019 | 0.012 |
| Open science | 0.006 | 0.021 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".