Investigating the relationship between humor and difficulty in regulation of emotions and alexithymia in students
Bibliographic record
Abstract
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.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".