What are Canadian Medical Students Learning about Health Informatics
Bibliographic record
Abstract
Objective: To perform an environmental scan of health informatics teaching practices in Canadian med-ical schools as part of a quality improvement initiative at Dalhousie University’s Faculty of Medicine. Methodology: We contacted undergraduate medical education staff at all seventeen Canadian medical schools and, via e-mail, asked open-ended questions about informatics content in the curriculum, timing of content delivery, teaching methodologies and informatics faculty. Results: Sixteen of seven-teen medical schools answered our queries. Each school identified curricular content on information literacy and evaluation of evidence but identified no formal core curriculum in health informatics. Conclusions: As of 2009, health informatics had not yet penetrated formal undergraduate medical curricula in Canada. Efforts to introduce health informatics initiatives should take into account the lack of understanding of the discipline of health informatics by educators and the densely packed nature of medical curricula. A new Canadian project, involving the Association of Faculties of Medicine of Canada and Canada Health Infoway, offers promise for building new health informatics curricula within undergraduate medical education.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".