Whose record is it anyway? Putting patients' interests at the heart of the implementation and use of electronic medical records.
Bibliographic record
Abstract
With the dawn of electronic medical records (EMRs) and patient portals, there is an unprecedented opportunity to provide truly collaborative patient-centred care. These tools can promote communication between healthcare providers and patients, improve chronic disease management and enable patients to become active members in the healthcare delivery system, but only if the tools work for everyone involved--including patients. Without patient consultation and input, there will be limitations in the ways in which physicians and patients are able to capitalize on these tools. Decision-makers must begin to enact their commitment to collaborative patient-centred care by engaging patients in discussions related to EMR design, implementation and use.
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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 | 0.051 | 0.225 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.018 | 0.039 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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".