Using a Lego<sup>TM</sup>-Based Communications Simulation to Introduce Medical Students to Patient-Centered Interviewing
Bibliographic record
Abstract
PURPOSE: Teaching patient-centered interviewing skills to medical students can be challenging. We have observed that 1st-year medical students, in particular, do not feel free to concentrate on the interviewing skills because they are preoccupied with complicated technical medical knowledge. The Lego simulation we use with our 1st-year students as part of a professional-skills course overcomes that difficulty. SUMMARY: The Lego activity is a role play analogous to a doctor-patient interview that uses identical sets of Legos for the "doctor" and for the "patients" and a small construction that represents a patient history. CONCLUSIONS: With a simple questionnaire, data were collected from students at different points during instruction. Results indicate that the Lego activity was very effective in helping students learn the importance of open-ended questioning. It also was rated as highly as the very dynamic interactive part of the instructional session. The effectiveness of the Lego activity may be due to the properties of analogies.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".