Study on the Relationship Between Emotion Tropism and Expressivity of College Students and Their Mental Health
Bibliographic record
Abstract
The method of scaling is adopted to study the relation between emotional tropism (consisting of positive and negative affective variables), emotional expressivity and mental health by doing questionnaires from 263 college students in Chongqing City. The differences test shows that: (a) Males express more negative affect than females and are not as good as women in expressing their feelings, but no significant gender differences in positive affect and feeling of happiness is observed; and freshmen share more feeling of happiness than seniors, but no significant differences between grades in their expression of positive or negative affect and emotions are observed; (b) Correlation analysis shows that feeling of happiness and other positive affect have significant negative correlation with most of the psychological symptom factors, while negative affect have significant positive correlation with all the psychological symptom factors; and emotional expressivity shows significant negative correlation with interpersonal sensitivity, hostility, depression, anxiety and other factors; (c) Regression analysis further shows that negative affect exerts significant regression effect on obsession, interpersonal relationship, depression, anxiety, hostility, bigotry and other factors, and emotional expression exerts regression effect on interpersonal sensitivity, hostility, depression, anxiety and other factors.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".