Alexithymia, anger and anger expression styles as predictors of psychological symptoms
Bibliographic record
Abstract
Objective: A review of the literature shows that alexithymia, anger, and anger expression styles have been important variables in predicting psychological symptoms. Furthermore, alexithymic characteristics may cause various difficulties in anger and its expression. From this point of view, the aim of our study was to investigate the extent to which alexithymia, anger, and anger expression styles predicted psychological symptoms in the university sample. Method: The present study included 434 students (244 women, 190 men) from different departments of Hacettepe University. In addition to the Demographic Information Form, participants were administered the 20-item Toronto Alexithymia Scale (TAS-20) to assess the presence of alexithymic characteristics; to evaluate their anger and anger expression styles, the State-Trait Anger Expression Inventory (STAXI) was used, and participants’ psychological symptoms were examined using the Brief Symptom Inventory (BSI). After carrying out a correlation analysis to evaluate the relationships between all variables of the study, hierarchical regression analysis was conducted to investigate the degree to which alexithymia, anger, and anger expression styles predicted psychological symptoms. Results: According to the regression analysis, it was concluded that alexithymia, trait anger, and anger-in positively predicted psychological symptoms. Conclusion: Our study indicates that alexithymic characteristics, anger, and anger expression styles explain psychological symptoms. Additionally, it emphasizes the benefit of addressing alexithymic characteristics, the frequency of anger experience, and healthy ways of anger expression simultaneously and as a whole rather than individually in psychotherapies aiming to reduce psychological symptoms, even in persons that do not require a diagnosis.
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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 | 0.000 |
| 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".