Examining the roles of depression, pain catastrophizing, and self-efficacy in quality of life changes following chronic pain treatment
Bibliographic record
Abstract
Background Adults with chronic pain have a lower quality of life (QOL) compared to the general population. Chronic pain requires specialized treatment to address the multitude of factors that contribute to an individual’s pain experience, and effectively managing pain requires a biopsychosocial approach to improve patients’ QOL.Aim This study examined adults with chronic pain after a year of specialized treatment to determine the role of cognitive markers (i.e., pain catastrophizing, depression, pain self-efficacy) in predicting changes in QOL.Methods Patients in an interdisciplinary chronic pain clinic (N = 197) completed measures of pain catastrophizing, depression, pain self-efficacy, and QOL at baseline and 1 year later. Correlations and a moderated mediation were completed to understand the relationships between the variables.Results Higher baseline pain catastrophizing was significantly associated with increased mental QOL (b = 0.39, 95% confidence interval [CI] 0.141; 0.648) and decreased depression (b = −0.18, 95% CI −0.306; −0.052) over a year. Furthermore, the relationship between baseline pain catastrophizing and the change in depression was moderated by the change in pain self-efficacy (b = −0.10, 95% CI −0.145; −0.043) over a year. Patients with high baseline pain catastrophizing reported decreased depression after a year of treatment, which was associated with greater QOL improvements but only in patients with unchanged or improved pain self-efficacy.Conclusions Our findings highlight the roles of cognitive and affective factors and their impact on QOL in adults with chronic pain. Understanding the psychological factors that predict increased mental QOL is clinically useful, because medical teams can optimize these positive changes in QOL through psychosocial interventions aimed at improving patients’ pain self-efficacy.
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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 | 0.014 | 0.003 |
| 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".