Technology and clinician-learner interaction: How is the introduction of a new electronic health record expected to affect educational practice?
Bibliographic record
Abstract
Abstract IntroductionElectronic health records (EHRs) are increasingly common platforms used in medical settings to capture and store patient information, but very little has been published about how EHR implementation affects educational practice from the point of view of clinician-learner interactions. This research sought to examine how EHR implementation is anticipated to affect clinician-learner interactions and, in turn, impact upon educational priorities and outcomes. MethodsSemi-structured interviews were conducted with a group of practicing oncologists who work in outpatient clinics while also providing education to medical student and resident trainees. Data regarding perceived impact on the teaching dynamic between clinicians and learners were collected prior to implementation of an EHR. ResultsPhysician educators expected EHR implementation to influence the learning they themselves normally gain through teaching interactions as well as their engagement in teaching. Additionally, EHR implementation was expected to influence learners by changing what is taught, what is modelled, and their role in both clinical care and the educational dynamic. ConclusionUnderstanding the concerns clinicians have about EHR implementation both offers potential to enable changes to be made that could minimize disruptions caused by implementation and provides a foundation from which to assess actual educational impacts.
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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 | 0.021 | 0.105 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".