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Record W2016736013 · doi:10.1207/s15328015tlm1302_8

Using a Lego<sup>TM</sup>-Based Communications Simulation to Introduce Medical Students to Patient-Centered Interviewing

2001· article· en· W2016736013 on OpenAlex
Sheila Rutledge Harding, Marcel D’Eon

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueTeaching and Learning in Medicine · 2001
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsInterviewSession (web analytics)Medical educationPsychologyComputer scienceMedicineWorld Wide Web

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.616
Threshold uncertainty score0.914

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.124
GPT teacher head0.467
Teacher spread0.344 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it