Structural Equation Model of Chronic Pain Based on Mindfulness and Adherence to Treatment with the Mediating Role of Pain Catastrophizing in Women
Bibliographic record
Abstract
Introduction: Pain is a necessary factor for survival and motivates us to run away from danger and seek care. The present study aimed to explain the fit of the structural equation model of chronic pain based on mindfulness and adherence to treatment with the mediating role of pain catastrophizing in women. Method: This descriptive-correlational study employed structural equation modeling. The statistical population included all women referred to the pain clinics of Shiraz, Iran, in 2024 with complaints of musculoskeletal pain. In total, 384 cases were selected using a convenience sampling method. The data were collected using the McGill Pain Questionnaire, Five-Facet Mindfulness Questionnaire (Baer et al.), Treatment Adherence Questionnaire (Madanlu), and Pain Catastrophizing Scale (Sullivan et al.). The obtained data were then analyzed using structural equation modeling through SPSS software (version 26) and AMOS software (version 24). Results: The results of structural equation modeling indicated a good fit of the model with experimental data. Moreover, it indicated the significant effect of the coefficient of the standard direct path of mindfulness on the experience of chronic pain. The indirect path of mindfulness on the experience of chronic pain with the mediating role of pain catastrophizing was also significant (P<0.05) in this study. Additionally, the results showed the significant effect of direct and indirect standard path coefficient of treatment adherence with the mediating role of pain catastrophizing on chronic pain experience (P<0.05). Conclusion: According to the obtained results, mindfulness and treatment adherence with the mediating role of pain catastrophizing play an important role in explaining chronic pain. It is worth mentioning that these variables have a significant contribution in predicting pain in patients with chronic pain.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".