Self Management Behaviors in Rheumatoid Arthritis Patients and Associated Factors in Tehran 2013
Bibliographic record
Abstract
INTRODUCTION: Rheumatoid Arthritis (RA) is a systemic, autoimmune and inflammatory disease with an unknown etiology that is associated with progressive joint degeneration, limitation of physical activity and disability. The aim of the study was to evaluate self-management behaviors and their associated factors in RA patients. MATERIAL & METHOD: This cross-sectional study was performed in 2013 on185 patients in Iran. Data were selected through convenient sampling. The collected data included demographic variables, disease related variables, Arthritis Impact Measurement Scale 2 (AIMS-2SF), and Self-Management Behaviors (SMB). Data were analyzed by SPSS17 using Spearman correlation and logistic regression test. RESULT: In this study drug management, regular follow-up, and food supplement were used as the most frequently applied SMB and aquatic exercise, diet, massage therapy, and relaxation were the least common SMBs. Age, education, health status, occupation, marital status, sex, DAS28 (Disease Activity Score 28 joints), and PGA (Physician Global Assessment) were significantly related with SMB. CONCLUSION: The result of the study highlight the influence of demographic variables, health status, and disease related data on SMB. Thus, more studies are required to find factors influencing SMB in order to improve SMB.
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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 | 0.000 | 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) | 0.001 | 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".