Interprofessional learning and virtual communities: An opportunity for the future
Bibliographic record
Abstract
As various agencies increasingly advocate interprofessional care (IPC), it is paramount that the educational implications of this approach are considered. Interprofessional learning (IPL) is necessary for IPC and this paper argues that an emerging educational model, narrative-based virtual communities (VCs), meets this goal. We therefore argue for the fusion of narrative pedagogy with the VC approach to further the IPL agenda. Using stories to teach is not new. Technological innovations now make the possibility of using narrative, a way to enable students to experience greater reality in complex situations. Recently, two multimedia VCs have been developed. Here, we review the use of "The Neighborhood" and "Stilwell", as IPL tools. Early evaluation of these communities has been very positive and they offer a unique and innovative approach to IPL in ways that immerse learners from many professions into the context of the lives of individuals requiring health and social care, and the people who provide that service. Thus, it is possible to more fully realize and teach about collaboration and partnerships among professionals and patients.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".