Using virtual communities to promote interprofessional education: a North American / British cooperative venture
Bibliographic record
Abstract
Education today is moving away from didactic, classroom based, theoretical approaches towards learning experiences that mimic real clinical environments. Case studies and problem based learning (PBL) were innovative techniques that facilitated interprofessional learning. However they suffer from the limitations of being static and isolated from the complexity of everyday life. The focus tends to be on a problem rather than the person and their context, whilst the dynamic and complex nature of interprofessional working is not captured by static case studies. Our workshop will demonstrate a different approach through the development of dynamic, virtual communities, which serve as enhanced clinical practice environments for various health care professions. We have shared ideas and expertise in the creation of three such communities in the UK, USA and Canada each representing a small but diverse neighborhood. Our narrative pedagogy model uses a multimedia approach to bring our communities to life for students. We also work closely with service delivery agencies to ensure maximum realism, this allows us to explore interprofessional working as a key theme. Whether working online or in class, students access these interactions between health professionals, patients and families and base much of their learning on the problems and challenges they present. Through this interactive virtual clinical practice students learn with, from and about one another in ways similar to actual clinical situations. Interprofessional learning is facilitated by this context rich approach which produces re-usable resources, enhancing the sustainability of the model. Nursing, paramedic science, imaging science, social work and mental health students are currently involved in learning together whilst other agencies such as schools and the police are becoming involved. We will explore with participants how virtual communities could be used to facilitate their own interprofessional learning requirements, using our own experiences, evaluations and research findings as resources.
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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 | 0.012 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".