Precepting in the age of electronic medical records: A primer for clinical teachers
Bibliographic record
Abstract
Background The use of Electronic Medical Records (EMR) is increasingly common. Little is known about the influence of EMR on resident learning, and little guidance is available for clinical teachers in the EMR setting. Objectives This study aims to examine the impact of EMR on Memorial University of Newfoundland (MUN) Family Medicine resident learning and to develop a teaching primer for clinical teachers. Methods Impact of EMR on resident learning was evaluated using a web survey and focus group discussions. Introductory letters with consent form and link to web survey were emailed to all MUN Family Medicine residents and Academic Family Medicine faculty preceptors. To achieve desirable response rate the web survey was sent three times over the 20-week survey period. Response frequencies were descriptively analyzed. Concordance and discordance between faculty and residents were evaluated. Six residents and four faculty preceptors participated in the focus group discussions. FGDs were analyzed using thematic analysis. Recurrent themes arising from the web survey and focus groups were compiled with results from the literature review to form a primer for clinical teaching. Results 34 of the 43 eligible residents (79%) and 16 of the 17 eligible faculty (95%) completed the survey. Approximately 70% of residents and 69% of faculty agreed that EMR generally improves resident learning. Impairment of resident communication with patients was rated as the most frequent deficit (50% and 36% respectively. rho= -0.25). The FGDs affirmed the web survey results that EMR generally improves resident learning and preceptor feedback, but noted that improvements can be made using targeted communication training, avoiding EMR distraction when giving feedback, and avoidance of resident over-reliance on EMR templates and prescribing functions. Data is collated with a review of the literature in the form of a clinical primer to guide clinical teachers. Conclusions The rise of EMR has subtly changed the clinical supervision and instruction of medical learners. The results of this study contribute to the development of teaching strategies for enhancement of resident learning in the EMR setting. Targeted training for clinical teachers in the EMR setting may be of benefit to teachers and learners.
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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 | 0.046 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.010 | 0.018 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".