Examination of the Relationships among Psychological Symptoms, Alexithymia and Emotional Regulation: Mediating Role of Emotion Regulation
Bibliographic record
Abstract
Objectives: The current study aims to explore (1) the relationship among alexithymia, emotional regulation, and various psychological symptoms factors; (2) the mediating role of emotion regulation strategies in the relationship among alexithymia and psychological symptoms; and (3) whether inpiduals with alexithymia high and low scores differ in psychological symptoms and emotional regulation. Method: In this study that consists of 319 university students reached through convenience sampling method, a cross sectional non-experimental design is used. A demographic information form, the Toronto Alexithymia Scale (TAS-20), Brief Symptom Inventory (BSI) and Emotion Regulation Scale (ER) were used for data collection. Results: TAS-20 Scores were moderately positively correlated with all factors of BSI and the BSI-General Severity Index (GSI) and the ER-suppression subscale while negatively correlated with the ER- cognitive reappraisal factor. Further analysis showed that ER-suppression subscale mediated the relationship between alexithymia and the BSI-GSI while ER-cognitive reappraisal subscale did not mediate it. Finally, a series of Mann-Whitney U tests indicated that inpiduals with high levels of alexithymia differed from those with low levels in all study variables. Conclusion: The results show that ER-suppression subscale has a mediating role between alexithymia and psychological symptoms as well as that alexithymia and emotional regulation are effective factors for predicting psychological symptoms. Finally, the findings imply that emotion regulation is associated with alexithymia and psychological symptoms and contribute to the development these 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".