Educational Electronic Health Records at the University of Victoria: Challenges, Recommendations and Lessons Learned
Bibliographic record
Abstract
There has been an acknowledged need for the integration of health technologies such as the electronic health record system (EHR) into health professional education. At the University of Victoria we have been experimenting with different models, architectures and applications of educational EHRs in the context of training health informatics, medical, and nursing students who will ultimately use this technology in their daily practice upon graduation. Our initial work involved the development of a Web-based portal that contained a number of open source EHRs and is described in this paper. In addition to the technical side, considerations around pedagogy and how best to integrate such technology into the classroom and educational experience are discussed. Finally, challenges and lessons learned from our decade of work in this area are discussed.
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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 | 0.037 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".