Stepping Up to the Plate: An Agenda for Research and Policy Action on Electronic Medical Records in Canadian Primary Healthcare
Bibliographic record
Abstract
Building on a previous study, which identified gaps in primary healthcare electronic medical record (EMR) research and knowledge, a one-day conference was held to facilitate a strategic discussion of these issues.This paper offers a multi-faceted research agenda and suggestions for policy actions as a way forward in bridging the gaps.One facet focuses on the need for research.The second facet focuses on harnessing the knowledge of primary healthcare EMR stakeholders.Finally, the third facet focuses on policy actions.This paper offers consensus-based suggestions with a view to improving the overall primary healthcare EMR landscape in Canada. RésuméEn réponse à une première étude qui identifiait des lacunes dans la recherche et les connaissances concernant les dossiers médicaux électroniques (DME) dans les soins de santé primaires, une conférence a eu lieu afin de permettre une discussion stratégique sur cette situation.Cet article présente un programme de recherche multifacette et des suggestions d' orientation afin de combler ces lacunes.La première facette souligne le besoin de faire de la recherche.La seconde facette porte sur la canalisation des connaissances des parties prenantes liées aux DME dans les soins de santé primaires.Finalement, le troisième aspect soulève
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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 | 0.122 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.015 |
| Science and technology studies | 0.043 | 0.024 |
| Scholarly communication | 0.041 | 0.021 |
| Open science | 0.008 | 0.013 |
| Research integrity | 0.021 | 0.015 |
| Insufficient payload (model declined to judge) | 0.007 | 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".