Application of the Technological Pedagogical Content Knowledge Framework in Integrating an Educational EMR into Health Informatics Education
Bibliographic record
Abstract
The discipline of health informatics is highly immersed in information technology, specifically health information systems. Students graduating from Bachelor degree programs in health informatics are expected to be familiar with a variety of systems upon entering the workforce. The adoption of systems like electronic medical records is on the rise across Canada, therefore it would be highly beneficial for students to have exposure to such systems in their coursework. While some individual instructors have done this to some extent on an ad hoc basis, formal strategies for EMR integration do not exist. A prominent framework for technology integration in learning that has been applied in many scientific disciplines is the Technological Pedagogical Content Knowledge (TPCK) framework. This paper describes how TPCK was used and applied as the guiding conceptual framework for exploring the integration of an educational EMR into undergraduate health informatics 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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".