Untying the Interprofessional Gordian Knot: The National Collaborative for Improving the Clinical Learning Environment
Bibliographic record
Abstract
The National Collaborative for Improving the Clinical Learning Environment (NCICLE) is a growing group of over 40 organizations representing a variety of health professions. NCICLE is beginning a discussion of issues related to culture in health care, specifically how the current culture inhibits optimal outcomes, and the discordance between current early interprofessional education (IPE) curricula in health professions schools and traditional practice models in health care. In October 2017, the Accreditation Council for Graduate Medical Education and the Josiah Macy Jr. Foundation sponsored an NCICLE symposium on optimizing interprofessional clinical learning environments. In this Invited Commentary, the authors observe that interprofessional practice and education is a decades-long field that has presented a "Gordian knot" of intractable, complex problems to solve because medicine has often not been at the table for conversations about IPE. The NCICLE symposium represented an important opportunity for medicine to signal that finding new solutions for unraveling the interprofessional Gordian knot and creating optimal clinical learning environments requires meaningful participation from all health professions. Those solutions need to build on the long history of experience and research in IPE and collaborative practice. After the NCICLE symposium provided a promising beginning, the authors propose three essential issues and one key practical step forward to move the interprofessional agenda forward.
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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 | 0.035 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.022 |
| Scholarly communication | 0.017 | 0.019 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.053 | 0.069 |
| 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".