A Model for Psychological Distress Based on Insecure Attachment Mediated by Alexithymia in Students at Islamic Azad Universities in Tehran
Bibliographic record
Abstract
Background: Individuals with alexithymia have a limited ability to adapt to stressful situations, leading to high levels of psychological distress, anxiety, and stress symptoms. Objectives: This study aimed to investigate the role of insecure attachment in psychological distress within Islamic Azad Universities of Tehran in 2020 by mediating alexithymia. Methods: The structural equation modeling (SEM) was used as the primary research method in this study. The study population included all university students between 20- and 40-years old studying at the Islamic Azad Universities of Tehran in the 2019 - 20 academic year. A total of 305 students was selected as samples using the multi-stage cluster sampling method. Several research tools were used, including the Depression Anxiety Stress Scale, the Experiences in Close Relationships-Revised (ECR-R), and the Toronto Alexithymia Scale (TAS-20). The data were analyzed using a correlation matrix in SPSS statistical software version 24 and SEM in Amos-26. Results: The results indicated that the insecure attachment style had a direst positive effect on the states of psychological distress (anxiety, stress, and depression) (P < 0.001). Alexithymia also played a mediating role in insecure attachment affecting psychological distress (P < 0.001). Conclusions: Results revealed in the correlation between insecure attachment and states of psychological distress mediated by the alexithymia. Given the importance of attachment style and alexithymia in the development of anxiety, stress, and depression, therapists are recommended to take these variables into account.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".