Interprofessional Skills Learning Guide: A Multimedia E-Book for Small-Group or Individual Learning
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
INTRODUCTION: Redefining learning space beyond physical classrooms with fixed resources is necessary to address challenges of interprofessional learning in a clinical setting. This multimedia e-book introduces recognized team skills of shared mental models, situational awareness, and the SBAR (situation, background, assessment, and recommendation) communication tool for individual or small-group learning. The e-book was derived from work done to develop an interprofessional small-group interactive learning tool for use in a clinical environment where resources, including meeting space, time, and facilitators, were limited. It is designed for individuals early in their clinical training but who have had previous clinical experience. METHODS: Utilizing readings, a series of videos, and reflective questions, a virtual narrator guides learners through an interactive case regarding a virtual chronic obstructive pulmonary disease patient preparing for discharge. RESULTS: Thirty-two responders evaluated the learning content as being clinically relevant. Comments encouraged all health care providers to become familiar with these interprofessional tools. DISCUSSION: Electronic, human, and space resources are often limited, especially in the clinical/education interface of the hospital or clinic environment for embedded interprofessional learning opportunities. The multimedia e-book provides a stand-alone learning resource for individuals or small groups of the same or different professions, with the opportunity for interactive learning with minimal space and human resource requirements.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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