The Mediating Role of Cognitive Emotion Regulation Strategies in the Relationship Between Early Maladaptive Schemas, Alexithymia, and Emotional Intelligence With Somatic Symptoms in People With Somatic Symptoms Disorder
Bibliographic record
Abstract
Objective: The present study was done to investigate the mediating role of cognitive emotion regulation strategies in the relationship between early maladaptive schemas, alexithymia, and emotional intelligence with the somatic symptoms in people with somatic symptom disorder (SSD). Methods: The research population consisted of 360 people with SSD referred to the psychosomatic department of Taleghani Hospital in Tehran in 2021 and were referred by psychiatrists and psychologists of medical centers selected by an accessible sampling method. The participants were asked to complete the Toronto alexithymia scale, early maladaptive schemas questionnaire, Bar-on emotional intelligence scale, cognitive emotion regulation strategies scale, and Takata and Sakata psychosomatic symptom scale. Data were analyzed by correlation analysis and structural equation modeling test Results: The findings indicated that the hypothesized model had a good fit with the data. The results of the path analysis showed that cognitive emotion regulation strategies play a mediating role in the relationship between alexithymia and maladaptive schemas with SSD. Also, the mediating role of cognitive emotion regulation strategies between emotional intelligence and somatic symptoms was not significant. Conclusion: Based on the findings of the research, it can be concluded that maladaptive schemas and alexithymia with somatic symptoms have no linear and simple relationship, but other variables, such as cognitive emotion regulation strategies play a mediating role in this relationship. Also, the findings of the current research can be used in order to prevent and understand the underlying etiologies and treatment of SSD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".