Presenting a Model of Psychological Vulnerability Based on Alexithymia in Multiple Sclerosis Patients: The Mediating Role of Anxious Thoughts
Bibliographic record
Abstract
Among the stress-inducing events that impact an individual's psychological well-being, chronic illnesses such as multiple sclerosis (MS) are significant. These patients not only face physical challenges but also encounter numerous psychological issues that further exacerbate their illness and contribute to their psychological vulnerability. The current study aimed to determine the indirect effect of Alexithymia on the psychological vulnerability of MS patients, mediated by anxious thoughts. The study method was descriptive-correlational. The population consisted of members of the MS Society of Tehran in 2022. A total of 312 patients were selected using convenience sampling (n = 312). Data collection tools included the Toronto Alexithymia Scale (Bagby, Parker, & Taylor, 1994), the 25-Item Symptoms Checklist by Nejarian and Davoudi (2001), and the Wells Anxious Thoughts Questionnaire (1994). Data analysis using structural equation modeling indicated that the model of psychological vulnerability based on Alexithymia with the mediating role of anxious thoughts fits well. Also, the effect of Alexithymia on psychological vulnerability (β = 0.371; p < 0.001), the effect of anxious thoughts on psychological vulnerability (β = 0.463; p < 0.001), and the effect of Alexithymia on anxious thoughts (β = 0.367; p < 0.001) were positive and significant. The findings of this study could guide the development of comprehensive therapeutic models for patients with MS and their emotional issues.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 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".