Electronic health record (EHR) projects in Canada: participation options for Canadian health librarians
Bibliographic record
Abstract
Research question: What are the major issues in the implementation of electronic health record (EHR) systems in Canada and what competencies can Canadian health librarians bring to their participation in these projects? Data sources: Health informatics and library science databases were searched for EHR literature. Grey literature was located at Canada Health Infoway's website, on provincial and federal government websites, and by searching online news websites. Study selection: The data sources were searched for journal articles, reviews, newspaper articles, government publications, interviews, grey literature, dissertations, editorials, and discussions. Data extraction: Data were extracted from the data sources using search strategies and keywords outlined in Appendix A. Due to the scope and focus of this paper, search terms were selected to emphasize a Canadian context; in particular, a British Columbian perspective in regards to EHR implementation. Results: This paper draws on a body of evidence to discuss EHR implementation issues and health librarian involvement in Canada. There is a growing body of research in the American biomedical literature about health librarian participation in EHR implementation but little in the Canadian health literature. Conclusion: This is the first paper of its kind that proposes new roles for Canadian health librarians in EHR implementation. Health librarians’ expertise in organizing and retrieving information makes them ideally suited for providing evidence-based medicine or consumer health information embedded directly in EHRs. Further research is needed to demonstrate the value of health librarians on EHR project teams.
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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 | 0.028 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.015 |
| Science and technology studies | 0.024 | 0.004 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".