Exploring the Educational Value of Popular Culture in Web-Based Medical Education: Pre-Post Study on Teaching Jaundice Using “The Simpsons”
Bibliographic record
Abstract
BACKGROUND: The potential of popular culture as a tool for knowledge delivery and enhancing engagement in education is promising but not extensively studied. Furthermore, concerns exist regarding learning fatigue due to increased reliance on videoconferencing platforms following the COVID-19 pandemic. To ensure effective web-based teaching sessions that maintain attention spans and enhance understanding, innovative solutions are necessary. OBJECTIVE: This study aims to evaluate the use of specific popular culture case studies to enhance student engagement in a web-based near-peer teaching session. METHODS: We delivered a web-based teaching session to undergraduate medical students in the United Kingdom. The session included clinical vignettes and single-best-answer questions using characters from "The Simpsons" television show as patient analogies for various causes of jaundice. A pre-post survey, employing a 7-point Likert scale, was distributed to gather data from participants. RESULTS: A total of 53 survey responses were collected. Participants reported significantly improved understanding of jaundice after the session compared to before the session (median 6, IQR 5-6 vs median 4, IQR 3-4.5; P<.001). The majority of participants agreed that the inclusion of "The Simpsons" characters enhanced their knowledge and made the teaching session more memorable and engaging (memorability: median 6, IQR 5-7; engagement: median 6, IQR 5-7). CONCLUSIONS: When appropriately integrated, popular culture can effectively engage students and improve self-perceived knowledge retention. "The Simpsons" characters can be used pedagogically and professionally as patient analogies to deliver teaching on the topic of jaundice.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".