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Record W2052455699 · doi:10.1111/sdi.12194

Laughter and Humor Therapy in Dialysis

2014· review· en· W2052455699 on OpenAlexaff
Paul N. Bennett, Trisha Parsons, Ros Ben‐Moshe, Melissa K. Weinberg, Merv Neal, Karen Gilbert, Helen Rawson, Cherene Ockerby, Paul Finlay, Alison M. Hutchinson

Bibliographic record

VenueSeminars in Dialysis · 2014
Typereview
Languageen
FieldPsychology
TopicHumor Studies and Applications
Canadian institutionsQueen's University
Fundersnot available
KeywordsLaughterMedicineContext (archaeology)PopulationAnxietyPsychological interventionPsychotherapistQuality of life (healthcare)DialysisPhysical therapyPsychiatryPsychologyNursing

Abstract

fetched live from OpenAlex

Laughter and humor therapy have been used in health care to achieve physiological and psychological health-related benefits. The application of these therapies to the dialysis context remains unclear. This paper reviews the evidence related to laughter and humor therapy as a medical therapy relevant to the dialysis patient population. Studies from other groups such as children, the elderly, and persons with mental health, cancer, and other chronic conditions are included to inform potential applications of laughter therapy to the dialysis population. Therapeutic interventions could range from humorous videos, stories, laughter clowns through to raucous simulated laughter and Laughter Yoga. The effect of laughter and humor on depression, anxiety, pain, immunity, fatigue, sleep quality, respiratory function and blood glucose may have applications to the dialysis context and require further research.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.043
GPT teacher head0.392
Teacher spread0.349 · 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

Citations75
Published2014
Admission routes1
Has abstractyes

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