Health among humorists: Susceptibility to contagious diseases among improvisational artists
Bibliographic record
Abstract
Abstract There is a widely held belief that humor contributes to better health, but the research on this topic yields mixed results. To assess the relationship between humor and health, we compared the susceptibility to various infectious diseases of 511 comedy performers (amateur improvisational artists) and a control group of 795 non-performers that were matched to the comedy performers sample in age and sex. Subjects reported the number of episodes and the total days they had had various infectious diseases. Contrary to the prevailing sentiment that humor boosts health, results showed that the comedy performer group reported more frequent contagious diseases and more days having these infections diseases, compared to the control group. Improv artists had significantly more infections and reported more days infected than the control group on respiratory infections, head colds, stomach or intestinal flu, skin infections, and autoimmune diseases. The control group had significantly more bladder infections with non-significant difference on days infected. Results held after controlling for BMI, age, number of antibiotics used and neuroticism. We found no evidence that humor positively contributes to health, and a career in a humor-related profession may be detrimental to one’s health. Our research highlights the complex relationship between humor and health outcomes.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".