Childhood traumatic events, alexithymia and perceived stress in patients with rheumatoid arthritis during the COVID-19 pandemic
Bibliographic record
Abstract
Rheumatoid arthritis (RA) is a chronic inflammatory disease, causing joint-swelling and pain. International literature highlights that patients with RA are more likely to report high levels of alexithymia, adverse childhood events (ACEs) and stress, but studies investigating the association between these dimensions are lacking. The general aim of the present study is to investigate the association between alexithymia, ACEs, and stress in RA patients and to highlight possible predictors of greater perceived stress. One hundred and thirty-seven female patients with RA (mean age = 50.74; SD = 10.01) participated in an online survey between April and May 2021. Participants completed a questionnaire for the collection of sociodemographic and clinical information, the 20-item Toronto Alexithymia Scale, the Adverse Childhood Events questionnaire and the 10-item Perceived Stress Scale. The correlational analysis highlighted several significant associations between the dimensions evaluated. Regression analyses showed that alexithymia, ACEs and the perceived health status have a predictive effect on the perceived stress of RA patients. More specifically, the role of difficulty in identifying feelings, and the physical and emotional neglect, has been highlighted. ACEs and high levels of alexithymia are common in RA clinical populations and seem to affect the wellbeing of these patients. The use of a biopsychosocial approach to RA treatment appears essential in achieving a better quality of life and illness control in this specific clinical population.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".