How to Build High-Quality Interprofessional Collaboration and Education in Your Hospital
Bibliographic record
Abstract
Interprofessional education (IPE) is an important contributor to ensuring interprofessional collaboration and, ultimately, improving the quality of health care. However, there is a gap in available resources on critical success factors for implementing intentional interprofessional learning experiences. The Interprofessional Collaborative Organizational Map and Preparedness Assessment (IP-COMPASS) is a quality improvement framework that provides a structured process to help health care organizations become better prepared to offer IPE. Essentially, it is designed to increase understanding of the attributes of organizational culture that can create an environment that is conducive to interprofessional learning. The IP-COMPASS was developed on the basis of a systematic multimethod approach to accessing existing knowledge and then tested for utility, feasibility, and validity. This article tells the story of the development and testing of the IP-COMPASS.
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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 | 0.021 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".