Effects of Cognitive-Behavioral Therapy on Empathy in Patients with Chronic Pain
Bibliographic record
Abstract
OBJECTIVE: Cognitive-behavioral therapy (CBT) is effective in patients with chronic pain. However, the efficacy of CBT for impaired empathy has not been studied in this population. We investigated the effect of CBT on empathy in patients with chronic pain. METHODS: Patients with severe chronic pain were recruited. Empathy was assessed before and after CBT using the Interpersonal Reactivity Index (IRI). The patients underwent eight sessions over the course of 1 month conducted. Additional symptoms were assessed using the Short-Form McGill Pain Questionnaire (SF-MPQ), Beck Depression Inventory, Beck Anxiety Inventory, World Health Organization Quality of Life Scale Abbreviated Version, and the Scale for Suicide Ideation. RESULTS: A total of 26 participants were included. Pre-CBT pain severity assessed using the SF-MPQ was significantly correlated with the IRI-empathic concern subscale score (p=0.021), and the relationship remained significant after adjusting for sex, age, education level, and marital status. After CBT, the IRI-perspective-taking subscale scores (p=0.004) increased significantly and the IRI-personal distress subscale scores (p=0.013) decreased significantly in all participants. The SF-MPQ scores increased significantly (p=0.021). CONCLUSION: CBT improved empathy in patients with chronic pain independent of its effect on pain, suggesting that CBT is useful for improving interpersonal relationships in patients with chronic pain.
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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 | 0.002 |
| 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) | 0.002 | 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".