Research on the Role of Humor in Well-Being and Health: An Interview With Professor Arnie Cann
Bibliographic record
Abstract
In this interview, Dr. Arnie Cann discusses his research and views on the ubiquitous role of humor in psychological health and well-being. The interview begins with Professor Cann recounting how he originally became interested in studying humor. He then reflects on the main findings associated with the wide variety of humor-related studies he has conducted over the years. In doing so, Dr. Cann provides suggestions and ideas for further research investigating the role of humor in health and well-being. Specific topic areas discussed include the use of humor in the workplace and other social domains, personality approaches to humor, humor and interpersonal processes, humor and psychopathology, and humor’s role in dealing with stress and well-being. One of the prominent themes in this interview is the clear recognition of sense of humor as a multi-dimensional construct that includes various components that may either be beneficial or detrimental to well-being. A further important theme is the major distinction between humor as an inherent personality construct versus humor that results from exposure to stimuli (e.g., a comedy film). Comments are also provided by Dr. Cann on how the positive affect stemming from humor may be of particular benefit to the individual. Also discussed is the recent move to more fully integrate contemporary humor research with positive psychology approaches. The interview concludes with Dr. Cann providing several recommendations regarding future theorizing and research on the role of humor in psychological well-being.
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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 | 0.018 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.023 |
| Insufficient payload (model declined to judge) | 0.002 | 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".