Competing discourses, contested roles: Electronic health records in medical education
Bibliographic record
Abstract
INTRODUCTION: The integration of electronic health records (EHRs) into medical education remains contested despite their widespread use in clinical practice. For medical trainees, this has resulted in idiosyncratic and often ad hoc methods of instruction on EHR use. The purpose of this study was to understand the currently fragmented nature of EHR instruction by examining discourses of EHR use within the medical education literature. METHODS: We conducted a Foucauldian critical discourse analysis to identify discourses of EHRs in the medical education literature. We found our texts through a systematic search of widely cited medical education journals from 2013-2023. Each text was analysed for recurring truth statements-claims framed as self-evidently true and thus not needing supporting evidence-about the role of EHRs in medical education. RESULTS: We identified three major discourses: (1) EHRs as a clinical skill and competency, emphasising training of physical interactions between learners, patients and computers; (2) EHRs as a system, emphasising the creation and facilitation of networks of people, technologies, institutions and standards; and (3) EHRs as a cognitive process, framed as a method to shape processes like clinical reasoning and bias. Each discourse privileged certain stakeholders over others and served to rationalise educational interventions that could be seen as beneficial in isolation yet were often disjointed in combination. CONCLUSIONS: Competing discourses of EHR use in medical education produce divergent interventions that exacerbate their contested role in contemporary medical education. Identifying different claims for the benefits of EHR use in these settings allows educators to make rational choices between competing educational directions.
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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 | 0.064 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.014 | 0.062 |
| Scholarly communication | 0.021 | 0.031 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".