How to Build High-Quality Interprofessional Collaboration and Education in Your Hospital
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it