Grounding theories of<i>W(e)Learn</i>: A framework for online interprofessional education
Bibliographic record
Abstract
Interprofessional care (IPC) is a prerequisite for enhanced communication between healthcare team members, improved quality of care, and better outcomes for patients. A move to an IPC model requires changing the learning experiences of healthcare providers during and after their qualification program. With the rapid growth of online and blended approaches to learning, an educational framework that explains how to construct quality learning events to provide IPC is pressing. Such a framework would offer a quality standard to help educators design, develop, deliver, and evaluate online interprofessional education (IPE) programs. IPE is an extremely delicate process due to issues related to knowledge, status, power, accountability, personality traits, and culture that surround IPC. In this paper, a review of the pertinent literature that would inform the development of such a framework is presented. The review covers IPC, IPE, learning theories, and eLearning in healthcare.
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How this classification was reachedexpand
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".