Exploring the Association of Alexithymia and Romantic Attachment with Quality of Life and Pain Perception in Systemic Sclerosis
Bibliographic record
Abstract
Introduction: Systemic Sclerosis (SSc) is a rare autoimmune connective tissue disease affecting the skin and internal organs, significantly impacting quality of life (QoL). Alexithymia and insecure romantic attachment may hinder coping and influence pain perception in chronic illness. This study aims to explore the associations among alexithymia, romantic attachment, pain perception, quality of life, and age in women with SSc, and to examine the predictive role of alexithymia, attachment dimensions and age on perceived physical quality of life using multiple linear regression analyses. Methods: Fifty women with SSc were recruited from a hospital in Rome. Participants completed a socio-demographic questionnaire, the 20-item Toronto Alexithymia Scale (TAS-20), the Experiences in Close Relationships–Revised (ECR-R), the Pain Visual Analogue Scale (pVAS), and the WHO Quality of Life Questionnaire–Brief Version (WHOQOL-BREF). Results: Participants had a mean age of 52.98 years (M = 52.98; SD = 12.87) and a mean SSc duration of 10.31 years (M = 10.31; SD = 8.84). TAS-20 scores negatively correlated with all QoL domains and total QoL (r = -0.303 to -0.672, all p .01). Difficulty in Describing Feelings correlated positively with pain VAS (r = .312, p .05). ECR-R Avoidance and Anxiety were negatively associated with several QoL dimensions. Difficulty in Describing Feelings emerged as a significant predictor of physical QoL dimension (B = −0.56, β = −.68, p .001). Overall, 18% scored above the TAS-20 clinical cut-off. Overall, 18% scored above the TAS-20 clinical cut-off. Conclusions: The findings suggest that higher levels of alexithymia and insecure attachment are associated with poorer quality of life and greater pain perception in women with Ssc. In particular, difficulties in describing feelings appear to play a key role in the physical dimension of quality of life, pointing to the clinical relevance of emotion verbalization processes in this population. These results support the relevance of affective regulation processes as potential targets for psychological assessment and intervention in Ssc patients and highlight the importance of multidisciplinary care.
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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".