Laugh Yourself into a Healthier Person: A Cross Cultural Analysis of the Effects of Varying Levels of Laughter on Health
Bibliographic record
Abstract
This cross-cultural study explored along with various personality factors the relationship between laughter and disease prevalence. Previous studies have only determined the effect of laughter on various health dimensions, whereas, this study quantified the level of laughter that was beneficial or detrimental to health. There were a total of 730 participants between the ages of eighteen and thirty-nine years. 366 participants were from Aurangabad, India (AUR), and 364 participants were from Mississauga, Canada (MISS). The participants were provided a survey assessing demographics, laughter, lifestyle, subjective well-being, life satisfaction, emotional well-being and health dimensions. In AUR, a beneficial effect of laughter was mediated through moderate levels (level two) of laughter, whereas both low (level one) and high (level three) levels had no effect. Similarly, in MISS, the beneficial effect was mediated through level two, but a negative effect was also seen at level three. This could be attributable to a higher prevalence of bronchial asthma in western countries. Laughter was associated with emotional well-being in MISS and life satisfaction in AUR, providing cross cultural models to describe the interactions between laughter and disease. This study validated the correlation between emotional well-being and life satisfaction, with a stronger correlation seen in MISS, suggesting that individualists rely more on their emotional well-being to judge their life satisfaction. In conclusion, there is a benefit to clinicians to incorporate laughter history into their general medical history taking. Future research should consider developing mechanisms to explain the effects of level two, determine specific systemic effects and obtain more samples to generalize the cross cultural differences.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".