Online interprofessional health sciences education: From theory to practice
Bibliographic record
Abstract
Online learning (e-learning) has a nascent but established history. Its application to interprofessional education (IPE), however, is relatively new. Over the past 2 decades the Internet has been used increasingly to mediate education. We have come past the point of "should we use the Internet for education" to "how should we use the Internet for education." Research has begun on the optimal development of online learning environments to support IPE. Developing online IPE should follow best practices in e-learning generally, though there are some special considerations for acknowledging the interprofessional context and clinical environments that online IPE is designed to support. The design, development, and deployment of effective online IPE must therefore pay special attention to the particular constraints of the health care worker educational matrix, both pre- and postlicensure. In this article we outline the design of online, interprofessional health sciences education. Our work has involved 4 educational and 4 clinical service institutions. We establish the context in which we situate our development activities that created learning modules designed to support IPE and its transfer into new interprofessional health care practices. We illustrate some best practices for the design of effective online IPE, and show how this design can create effective learning for IPE. Challenges exist regarding the full implementation of interprofessional clinical practice that are beginning to be met by coordinated efforts of multiple health care education silos.
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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 | 0.011 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.000 | 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".