The Relationship between Emotional Deficit and Pain in Patients with Rheumatoid Arthritis in Isfahan City
Bibliographic record
Abstract
Introduction: A link between emotional deficit and somatic factors has been widely established، yet little is known about different factors that may predict this relationship.The idea of psychopathology as a mediator has been supported by some pieces of evidencebut in fact, it has not been exactly scrutinized.Therefore, the present study examined the relationship between emotional deficit and pain severity in patients with rheumatoid arthritis. Methods: In this descriptive-correlational study، the target population included all patients with rheumatoid arthritis who referred to medical centers of Isfahan during spring 2012. A total number of 100 men and women with rheumatoid arthritis were selected via convenience sampling. A sociodemographic data form، Toronto Alexithymia Scale (TAS-20), Hospital Anxiety and Depression Scale (HADS) and rheumatoid arthritis pain scale (RAPS) were administered to each subject andrequired information was obtained. The study data was analyzed by SPSS-18، AMOS-18 software, Pearson Correlation, and Structural Equation Modeling methods. Results: Results indicated that the structural model fit clinical sample extremely well (chi2= 3.04; p= 0.218). Alexithymia، depression and anxiety were correlated with pain severity. In this model a latency variable (emotional deficit) was explored that predicted painseverity sowell(CFI, T,I، AGFI and GFI > 0.9). Conclusion: The study findings revealed thatemotional deficit hasan important role in the rheumatoid arthritis and the pain severity. The model can confirm those pieces of evidence indicating the psychological treatments included in multidisciplinary programs for this disorder.
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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 | 0.001 |
| 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 | 0.000 | 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".