A Pediatrician, a Resident, and a Medical Student Walk Into a Clinic: The Role of Humor in Clinical Teaching
Bibliographic record
Abstract
Never upset a pediatrician. They have very little patients. Egerton Yorick Davis It is often said that laughter is the best medicine (not true, it’s actually amoxicillin). But how often do we consider the power of laughter or, more broadly, the role of humor in our teaching of medicine? When executed well, humor can increase learner engagement and participation, cultivate relationships, foster an effective learning environment, and increase satisfaction during teaching encounters.1,2 It may also calm students’ anxieties and result in a more approachable dynamic between learner and instructor.3 There is some, albeit weak, evidence that humor can lead to improved learning, particularly if that humor is directly relevant to the topic.2 There is also evidence that the prudent use of the right kind of humor can increase engagement and participation during university lectures.1 Overall, humor is identified as a key contributor to teaching effectiveness. This article continues the series by the Council on Medical Student Education in Pediatrics by exploring the role humor plays in medical education. Humor is defined as a funny or amusing quality, whereas a sense of humor is defined as the ability to be funny or to be amused by things that are funny. Despite the seemingly innocuous nature of humor, historically humor has generally received a bad reputation in professional settings.4 Humor can increase group cohesion, bring individuals together, and alleviate tension. On the other hand, it can disparage others, create misunderstandings, or foster isolation and division and thus hinder the learning environment. So how is it that sometimes humor is amusing and other times it is offensive? … Address correspondence to Robert A. Dudas, MD, Department of Pediatrics, Johns Hopkins All Children’s Hospital, 601 5th Ave S, Room 5206, St Petersburg, FL 33701. E-mail: rdudas{at}jhmi.edu
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 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".