A comparison of humor styles, coping humor, and mental health between Chinese and Canadian university students
Bibliographic record
Abstract
Abstract This research compares the structure and correlates of the Humor Styles Questionnaire (HSQ) and Coping Humor Scale (CHS) in the Chinese context with those of Canadian samples. Chinese translations of the HSQ, CHS, and Symptom Checklist 90 (SCL-90) were administered to 354 Chinese university students (M = 23.4 years of age, SD = 3.6). As in the original Canadian samples, four humor factors were found in the HSQ: Affliative, Self-enhancing, Aggressive, and Self-defeating humor, and one factor was found in the CHS. The HSQ and CHS scale reliabilities in the Chinese sample were generally acceptable. Chinese participants, as compared to Canadian norms, reported significantly lower scores on the HSQ subscales and CHS, particularly on Aggressive humor. No significant gender differences were found on the four HSQ subscales in the Chinese sample, whereas Canadian males reported more use of Aggressive and Self-defeating humor than did females. Although no gender difference was found on Coping humor in the Canadian samples, Chinese males had significantly higher scores on this scale than did females. In both the Chinese and Canadian samples, younger participants reported more use of Affliative and Aggressive humor than did older ones. Affliative, Self-enhancing, and Coping humor were negatively correlated, while Aggressive and Selfdefeating humor were positively correlated with the subscales and General Symptomatic Index of the SCL-90. Regression results indicated that mental health is more strongly related to Self-enhancing, Self-defeating, and Coping humor than Affliative and Aggressive humor. Overall, the findings support the theoretical structure and usefulness of the HSQ and CHS in the Chinese context.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".