Adoption of Information Technology in Primary Care Physician Offices in Alberta and Denmark, Part 2: A Novel Comparison Methodology
Bibliographic record
Abstract
The seminal findings indicate that both Danish and Albertan physicians engaged with computers in the early 1990s; however, in Alberta the emphasis was on the recording of diagnosis and visit date for the purpose of payment for services by the provincial government, while in Denmark the emphasis was on creating an exchange mechanism between physicians utilizing existing standard protocols. In Denmark, physicians rapidly adopted locally provided EMRs, while in Alberta physicians retained paper records until the introduction of a standard process for reimbursing the costs of extended computer capability in 2001. While many of their peers in adjacent provincial or European Union country jurisdictions have languished with respect to EMR adoption, these two trajectories have led to nearly 100% of EMR adoption by general practitioners in Denmark as early as 2000 and 60% adoption by primary care physicians in Alberta by 2006. An evaluation of the similarities and differences points to various factors that have contributed to the rate of adoption of primary care physician office computing that may be important for future evaluations in other settings
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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 | 0.016 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.004 |
| 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".