Online Interprofessional Health Sciences Education: Designing Inter-institutional E-Learning
Bibliographic record
Abstract
Abstract The Institute for Interprofessional Health Sciences Educ ation (IIHSE/IEISS – the Institute) is a virtual learning institute established with health Canada funding in 2005. We are working collaboratively to construct online leaning for interprofessional health education. The IIHSE is innovating e learning on several levels by: • Creating online, asynchronous education materials to support interprofessional health sciences education for undergraduates • Creating these for professional, in-service education • Creating a collaborative inter-institutional structure for continued content creation • Inter-institutional access to electronic libraries and resources The IIHSE has been founded in order to promote interprofessional education (IPE) across institutions, faculties, practice sites and communities of practice. The Institute uses e-learning technologies to enact a distributed learning paradigm for the delivery of education. This involves the use of web-based teaching and learning tools for encouraging problem-based learning, reflective practice, and the creation of a community of practice around IPE and its transfer into collaborative practice within the healthcare system. This paper outlines the challenges and opportunities encountered in the design of online, interprofessional health sciences education that involves multiple educational and clinical service institutions. This includes issues associated with distributed authoring of content, copyright and intellectual property issues, online library access for students from multiple universities, student articulation among disparate institutions, technical and educational support for distributed content authors and students. The paper will discuss these issues in the context of creating an online, collaborative educational institute for fostering and promoting interprofessional health sciences education at the undergraduate and in-service levels.
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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 | 0.022 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 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".