Supporting Students With Electronic Health Record–Embedded Learning Aids: A Mixed-Methods Study
Bibliographic record
Abstract
BACKGROUND: Students often perceive workplace-based learning as disconnected from what they learn in medical school. Interventions that deal with this issue regularly involve feedback and/or learning aids. Feedback has frequently been encouraged in previous research, whereas the use of aids is less understood. OBJECTIVE: This study aims to investigate the added value of learning aids in making the connection and enhancing the transfer of learning between medical school and workplace-based learning. METHODS: First-year students in postgraduate general practice training participated in a mixed-methods study. Within a quasi-experimental design, two conditions were investigated: (1) students having access to electronic health record (EHR)-embedded learning aids and feedback and (2) students only receiving feedback. Semistructured interviews were conducted and analyzed according to the thematic analysis approach. RESULTS: =-0.511, P=.61, 95% CI -4.86 to 2.90). Nevertheless, students used the aids frequently and found them useful. Given that the aids were familiar to students and contained practice-based instructions in an easily accessible format, they were perceived as feasible to use during workplace-based learning. They also appeared to stimulate transfer of learning, self-confidence, reflection, and interaction between student and supervisor. CONCLUSIONS: Access to EHR-embedded learning aids offers additional support during, but also before and after, patient encounters. The aids can be easily implemented into workplace-based learning.
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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 | 0.015 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".