Expressing Emotions, Resilience and Subjective Well-Being: An Investigation with Structural Equation Modeling
Bibliographic record
Abstract
The present study examined the relationship between expressing emotions, psychological resilience and subjective well-being. The study was carried out with a total of 217 university students, of whom 94 were males and 123 were females, aged between 19 and 25 years. The data of the study were collected using the Emotional Expression Questionnaire, the Psychological Resilience Scale and the Subjective Well-Being Scale, respectively. The relationship between the variables of the study was analyzed via the methods of Pearson Correlation Coefficient and Structural Equation Modeling, and the mediating role of psychological resilience between emotional expression and subjective well-being was tested. The goodness-of-fit indices obtained from the structural equation modeling indicated that the model generated a good fit. According to the results, there was a significant relationship between “expressing emotions” and “psychological resilience” and between “psychological resilience” and “subjective well-being”. It was found that there was no significant relationship between expressing emotions and subjective well-being and that the variable of expressing emotions affected that of subjective well-being by means of the psychological resilience (tool) variable and the model tested was significant.
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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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".