Association between change in self-efficacy and reduction in disability among patients with chronic pain
Bibliographic record
Abstract
PURPOSE: This study aimed to investigate whether changes in psychosocial factors and pain severity were associated with reduction in disability due to pain among patients with chronic pain. We hypothesized that increased self-efficacy would reduce disability. PATIENTS AND METHODS: This longitudinal observational study included 72 patients. Patients' psychological and physical variables were assessed before and after 3 months of treatment. Demographic and clinical information were collected, including the Pain Disability Assessment Scale (PDAS), the Pain Self-Efficacy Questionnaire (PSEQ), the Hospital Depression and Anxiety Scale, and the Numeric Rating Scale (NRS) to assess pain intensity. First, univariate regression analyses were conducted to clarify associations between change in PDAS and sex, age, pain duration, changes in psychosocial factors (self-efficacy, anxiety, and depression) and change in pain intensity. Second, multivariate regression was conducted using the variables identified in the univariate analyses (PSEQ and NRS) to detect the most relevant factor for reducing disability. RESULTS: Univariate regression analyses clarified that changes in PSEQ (β = -0.31; 95% CI: -0.54--0.08, p = 0.008) and NRS (β = 0.24; 95% confidence interval [CI]: 0.01-0.47, p = 0.04) were associated with reduction in PDAS. Multivariate regression analysis demonstrated that change in PSEQ (β = 0.26; 95% CI: -0.50--0.02; p = 0.01) was associated with a reduction in disability, independent of change in NRS. CONCLUSION: These findings suggest improved self-efficacy is associated with reduced disability in patients with chronic pain, independent of reduction in pain intensity. Focusing on improvement in self-efficacy may be an effective strategy in chronic pain treatment in addition to pain relief.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".