The University of Victoria Interdisciplinary Electronic Health Record Educational Portal
Bibliographic record
Abstract
Use of Electronic Health Record (EHR) systems is increasing globally. However, adoption rates of Health Information Systems (HISs) continue to remain poor. To improve adoption rates, there is need to provide greater HIS experience to health professionals and informaticians in health and biomedicine during their undergraduate and graduate education. A recent review of the health professional educational curricula (i.e., medicine, nursing, allied health and health/biomedical informatics) revealed that they provide only limited exposure to EHRs. In response to this educational need, the authors have developed the University of Victoria Interdisciplinary Electronic Health Record Educational Portal (UVicIED-EHR Portal). This unique, web-based portal allows students of the health professions and practicing professionals to access and interact with a set of representative EHR HIS solutions using the web. The portal, which links to several EMRs, EPRs and PHRs, has been used by several health professional educational programs in medicine, nursing and health informatics. It provides practicing health and health/biomedical informatics professionals, for example, managers and directors, with opportunities to access and review EHR systems. The portal has been used successfully in the classroom, laboratory and with distance education to give hands-on experience with a variety of HISs and their components.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.211 | 0.061 |
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".