The Relationship Between Emotion Regulation Strategies and Lifestyle with Pain Severity in Patients with Chronic Musculoskeletal Pain in Pain Clinics of Mashhad
Bibliographic record
Abstract
The present study aimed to investigate the relationship between behavioral emotion regulation and lifestyle with pain intensity in patients with chronic skeletal pain disorder attending pain clinics in Mashhad. The study employed a descriptive-correlational research design. Both field methods (questionnaires) and library research (books and articles) were used for data collection. Additionally, the study is applied in nature, as its findings can be used to improve the status of the examined variables. The statistical population consisted of all patients who were diagnosed with chronic skeletal pain disorder by physicians and referred to pain clinics in Mashhad during the second quarter of 2023. Due to the inability to precisely count the population size, the sample size was estimated using the Tabachnick and Fidell formula (2007). Accordingly, a sample of 160 participants was selected using convenience sampling. Data were collected using the Behavioral Emotion Regulation Questionnaire by Craig and Garnefski (2019), the Lifestyle Questionnaire by Kern et al. (1997), and the McGill Pain Questionnaire (2009). Based on the results of the correlation test, there was a significant positive relationship between withdrawal and pain intensity (r = 0.168) and between ignoring and pain intensity (r = 0.159). Furthermore, a significant negative relationship was found between coping and pain intensity (r = -0.190), while a significant positive relationship was observed between cautiousness and pain intensity (r = 0.202).
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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".