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Record W1981487954 · doi:10.1037/0033-2909.127.4.504

Humor, laughter, and physical health: Methodological issues and research findings.

2001· review· en· W1981487954 on OpenAlexaff
Rod A. Martin

Bibliographic record

VenuePsychological Bulletin · 2001
Typereview
Languageen
FieldPsychology
TopicHumor Studies and Applications
Canadian institutionsWestern University
Fundersnot available
KeywordsLaughterPsychologyPain toleranceTraitPhysical healthComedyDevelopmental psychologySocial psychologyClinical psychologyMental healthPsychotherapistMedicineThreshold of pain

Abstract

fetched live from OpenAlex

All published research examining effects of humor and laughter on physical health is reviewed. Potential causal mechanisms and methodological issues are discussed. Laboratory experiments have shown some effects of exposure to comedy on several components of immunity, although the findings are inconsistent and most of the studies have methodological problems. There is also some evidence of analgesic effects of exposure to comedy, although similar findings are obtained with negative emotions. Few significant correlations have been found between trait measures of humor and immunity, pain tolerance, or self-reported illness symptoms. There is also little evidence of stress-moderating effects of humor on physical health variables and no evidence of increased longevity with greater humor. More rigorous and theoretically informed research is needed before firm conclusions can be drawn about possible health benefits of humor and laughter.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.018
metaresearch head score (Gemma)0.043
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.018
Threshold uncertainty score0.093

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.043
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0090.010
Science and technology studies0.0010.003
Scholarly communication0.0040.003
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.002

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.

Opus teacher head0.602
GPT teacher head0.630
Teacher spread0.028 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations571
Published2001
Admission routes1
Has abstractyes

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