Bibliographic record
Abstract
Welcome to the Fall 2014 edition of the Jefferson Interprofessional Education and Care Newsletter. It has been a busy Fall at Jefferson and we are excited to share several new developments which have been pushing the envelope in IPE. In October, we hosted our 4th biennial conference, Interprofessional Care for the 21st Century: Redefining Education and Practice. This year we had a record number of conference participants and presenters joining us from a variety of national and international academic and service organizations. Our keynote speakers, Dr. George Thibault, President, Josiah Macy Jr Foundation; Dr. Barbara Brandt, Director, National Center for Interprofessional Practice and Education at the University of Minnesota; Dr. John Gilbert, Principal & Professor Emeritus, University of British Columbia College of Health Disciplines, Co-Chair of the Canadian Interprofessional Health Collaborative; and a team from the Veterans Administration, including Dr. Malcolm Cox, Dr. Stuart Gilman, Dr. Richard Stark and Dr. Kathryn Rugen, collectively challenged and inspired us to re-conceptualize interprofessional education and collaborative practice opportunities for students as we prepare them for a healthcare delivery system that will focus on the triple aim of improving a patient’s care experience, improving the health of patient populations, and reducing the per capita cost of healthcare. One of the articles that follows will highlight the conference presentation of the innovative work of Dr. Susanne Boyle from the University of Glasgow, Scotland and her colleagues. Dr. Boyle’s team explored the area of augmented reality and its applicability to enhancing online interprofessional education through virtual communities.
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 | 0.003 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.416 | 0.376 |
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".