A Blended Model of Electronic Medical Record System Adoption in Canadian Medical Practices
Bibliographic record
Abstract
In this paper, we develop and validate a comprehensive theoretical model of electronic medical record (EMR) system adoption in Canadian medical practices. Canada lags other developed countries in the adoption of information technology (IT) in healthcare, and medical practice adoption of EMRs is particularly low. Most Canadian medical practices have the distinct feature of blending characteristics of both individual physicians and small clinics in private practice. We built a theoretical model combining individual-type and organizational-type perceptions (from one point of view) and opportunities and barriers (from another point of view) and tested it with 119 physicians from across Canada. Results show a reasonably valid model explaining 55.3 percent of the physicians’ intent to adopt EMRs in their clinics. We found that physicians would adopt EMRs if they saw these systems as first being easy to use and second as being useful. Physicians’ innovativeness regarding the use of new IT was an additional favoring factor. Conversely, physicians would choose not to adopt EMRs if they feared such systems would not perform as expected, would involve possible legal and privacy risks, would affect clinics’ productivity, and would not be a justified adoption altogether. Overall, we found that physicians saw more opportunities than obstacles in using EMRs in their practices.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".