Bibliographic record
Abstract
MS patients suffer from many psychological problems.The purpose of this study was to examine the severity of somatic, anxiety and depression symptoms and the role of emotion regulation strategies on predicting it in MS patients.In this research, 42 MS patients were availability selected in Nahavand and Malayer cities.The method research was correlation.The Cognitive Emotion Regulation Questionnaire (Garnefski et al, 2002) and Psychological Signs Scale (Dura et al, 2007) used for data collection.The Pierson correlation coefficient and multivariate regression was used for analyzing the data.Results showed that 42.8% of MS patient had summarization symptoms, 35.7% depression symptoms and 28.5% anxiety symptoms.Results of correlation indicated that there was meaningful correlation between positive refocusing, positive reappraisal, other-blame, self-blame, rumination, catastrophizing and acceptance and psychological signs.According to regression results, emotion regulations strategies explain approximately 69% the variance of psychological signs in MS patients, and the rumination, self-blame and acceptance have been respectively predicted the psychological signs in MS patients.The results supported the severity of psychological symptoms and meaningful relationship of emotional regulation strategies with those symptoms among MS patients and attending to the role of trainings based emotion regulation in reducing the psychological symptoms of MS patients is specially importance for therapists and researchers of health psychology.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.926 | 0.894 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".