Predicting Pain Perception Based on Psychological Distress in Patients with Rheumatoid Arthritis: The Mediating Role of Sleep Quality
Bibliographic record
Abstract
Background and Objective: Patients with rheumatoid arthritis (RA) often suffer from chronic pain due to the nature of the disease, and in addition to the disease itself, this pain can be aggravated under the influence of psychological fac- tors. The study aimed to predict pain perception based on psychological distress and the mediating role of sleep quality in people with RA. Materials and Methods: This research was conducted by path analysis, including 202 patients with RA who were se- lected using the convenience sampling method. The study instruments included McGill Pain Questionnaire (MPQ), Pittsburgh Sleep Quality Index (PSQI), and Depression, Anxiety, and Stress Scale (DASS-21). Descriptive statistics reported frequency, mean, standard deviation (SD), and Pearson correlation. In analytical statistics, path analysis was used. Data were analyzed using SPSS and AMOS software. Results: Psychological distress (anxiety, stress, and depression) had a direct, statistically significant effect on sleep quality. Sleep quality had a direct effect on pain perception. On the other hand, anxiety, stress, and depression, with the mediating role on sleep quality, had a significant influence on pain perception by 0.11, 0.12, and 0.09, respectively. Descriptive statistics showed that a significant correlation existed among independent, mediation, and criteria variables. The proposed predictive model had a good fit. Conclusion: Along with medical treatments, we need to pay attention to the role of psychological factors such as psychological distress, depression, and sleep quality in patients with RA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".