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Record W2793942712 · doi:10.22038/jfmh.2018.10456

Investigating the relationship between humor and difficulty in regulation of emotions and alexithymia in students

2018· article· en· W2793942712 on OpenAlexaboutno aff
Yousef Dehghani, Nozhatozaman Moradi, Fateme Tabnak, Seyed Ali Afshin

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2018
Typearticle
Languageen
FieldPsychology
TopicHumor Studies and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsAlexithymiaPsychologyEmotional regulationSocial psychologyDevelopmental psychologyCognitive psychology

Abstract

fetched live from OpenAlex

Introduction: Humor is an adaptive coping strategy that can be used as a way to cope with everyday stresses and communication. Therefore, the aim of this study is to investigate the relationship between humor and difficulty in regulation of emotion and alexithymia in students. Materials and Methods: The present study is correlational. The statistical population of the study is all students of the Persian Gulf University in Bushehr, 200 of which were selected using the cluster sampling method. They responded to the Sense of Humor Questionnaire (SHQ, a 25-item questionnaire), the Difficulties in Emotion Regulation Scale (DERS, 36-item questionnaire), and the Toronto Alexithymia Scale (TAS, a 20-item questionnaire). To analyze the data, the statistical method of simultaneous multivariate regression was used. Results: The results of the study shows that there is a positive and significant relationship between humor and alexithymia (r= -0.17, P=0.05), but there is no relationship between humor and difficulty in emotion regulation (r= -0.08, P=0.05). There is also a positive and significant relationship between alexithymia and difficulty in emotion regulation (r= -0.19, P =0.01). Conclusion: Based on the findings of this study, the effective importance of sense of humor in reducing emotional difficulty and alexithymia can be concluded.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.619

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.389
GPT teacher head0.594
Teacher spread0.205 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations2
Published2018
Admission routes1
Has abstractyes

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