Distilling the Progress of Electronic Medical Record (EMR) Usage by Canadian Physicians
Bibliographic record
Abstract
The main objective of this study is to assess the Canadian record-keeping system, with a focus on the actual use of Electronic Medical Records (EMRs) by physicians in healthcare settings. The assessment is based on quantitative data collected from four national surveys conducted in 2010, 2014, 2018, and 2021 by the National Physician Survey (NPS), the Canadian Medical Association (CMA), and Canada Health Infoway (Infoway). Findings indicate that 86.6% of Canadian physicians were using EMRs in their daily practice in 2021, representing a five-and-a-half-fold increase since 2010. Regarding usage rates by primary care type, there is a statistically significant difference between Family Physicians/General Practitioners (FP/GP) and other specialists, with specialists reporting higher EMR use than FP/GPs. Canadian healthcare policymakers have addressed organizational and technical barriers faced by physicians, by building a shared national vision and leadership, and enhancing coordination among various stakeholders and levels of government.
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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 | 0.009 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".