Effects of a patient’s name and image on medical knowledge acquisition
Bibliographic record
Abstract
PURPOSE: To assess whether there are differences in medical students' (MS) knowledge acquisition after being provided a virtual patient (VP) case summary with a patient's name and facial picture included compared to no patient's name or image. METHOD: 76 MS from four clerkship blocks participated. Blocks one and three (Treatment group) were provided case materials containing the patient's name and facial picture while blocks two and four (Control group) were provided similar materials without the patient's name or image. Knowledge acquisition was evaluated with a multiple-choice-question examination (CQA_K). RESULTS: Treatment group CQA_K scores were 64.6% (block one, n = 18) and 76.0% (block three, n = 22). Control group scores were 71.7%, (block two, n = 17) and 68.4% (block four, n = 19). ANOVA F-test among the four block mean scores was not significant; F (3, 72) = 1.68, p = 0.18, η2=0.07. Only 22.2% and 27.3% of the MS from blocks one and three respectively correctly recalled the patient's name while 16.7% and 40.9% recalled the correct final diagnosis of the patient. CONCLUSIONS: These results suggest that including a patient's name and facial picture on reading materials may not improve MS knowledge acquisition. Corroborating studies should be performed before applying these results to the design of instructional materials.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".