A Pilot Study of Computer-Based Simulation Training for Enhancing Family Medicine Residents' Competence in Computerized Settings
Bibliographic record
Abstract
We previously developed a prototype computer-based simulation to teach residents how to integrate better EMR use in the patient-physician interaction. To evaluate the prototype, we conducted usability tests with three non-clinician students, followed by a pilot study with 16 family medicine residents. The pilot study included pre- and post-test surveys of competencies and attitudes related to using the EMR in the consultation and the acceptability of the simulation, as well as 'think aloud' observations. After using the simulation prototypes, the mean scores for competencies and attitudes improved from 14.88/20 to 15.63/20 and from 22.25/30 to 23.13/30, respectively; however, only the difference for competencies was significant (paired t-test; t=-2.535, p=0.023). Mean scores for perceived usefulness and ease of use of the simulation were good (3.81 and 4.10 on a 5-point scale, respectively). Issues identified in usability testing include confusing interaction with some features, preferences for a more interactive representation of the EMR, and more options for shared decision making. In conclusion, computer-based simulation may be an effective and acceptable tool for teaching residents how to better use EMRs in clinical encounters.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".