A Conceptual Framework for Analyzing How Canadian Physicians are Using Electronic Medical Records in Clinical Care
Bibliographic record
Abstract
Our electronic medical record (EMR) case study research pursued a set of questions to provide Canadian physicians with practical information on best practices and lessons learned regarding implementation and use of EMRs in ambulatory clinical care. The study's conceptual framework included an EMR System and Use Assessment Survey, interview guide, transcription codes, observation guide and case study report template. The common message that emerged was that no clinic would return to paper-based charts after experiencing the benefits of EMR. In seeking to corroborate our findings with success factors in an EMR implementation meta-framework, we further investigated the role of information incentives as a key factor in sustainable EMR implementations. The sections of our conceptual framework that best enabled us to capture information incentives were the 12 survey questions about information quality, EMR adoption questions in the interview guide and a subset of 26 items from our transcription coding scheme that were linked to physicians quotations about knowing more about the patient when using the EMR than when using paper.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.033 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.014 | 0.030 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".