Bibliographic record
Abstract
Interprofessional education in preparation for the skills to execute teams and teamwork through interprofessional collaboration has been publicized and mandated by several professional associations through accreditation standards. The prerequisite is emphasized by the National Academy of Medicine (formerly the Institute of Medicine) as a mantra for successful healthcare outcomes. In response, the Interprofessional Education Collaborative (known as IPEC) published core competencies in 2011 with an update in 2016. While these statements are not each independently expressed in measurable terms, they stand as a compendium to guide interprofessional collaboration. To date, the literature does not reflect a comprehensive approach to explicating or interpreting these to be embraced more readily. Further, the literature to enlighten student education outstrips the literature to illuminate faculty education, though we acknowledge the work of the National Center for Interprofessional Practice and Education to inspire faculty education through a variety of platforms. Though the IPEC publications represent seminal work in the US, built on earlier work from the UK and others, its translation for faculty education applying a straight-forward, orderly, and methodical approach has not been done. Our attempt was to take one of the four (“4”) IPEC core competencies, Core Competency 3: Interprofessional Communication, and describe its underpinnings in a systematic way as another tool for faculty education. It may open the door to further expound on each competency statement to employ IPEC competencies within a healthcare community that includes students, faculty and post graduate professionals.
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.
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 | 0.040 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.011 | 0.027 |
| Scholarly communication | 0.026 | 0.047 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.011 | 0.031 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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".