Effectiveness of Cognitive Analytic Therapy on Anxiety Sensitivity and Pain Catastrophizing in Patients with Chronic Pain and Alexithymia
Bibliographic record
Abstract
Introduction: The present study was conducted with the aim of investigating the effectiveness of cognitive analytic therapy on anxiety sensitivity and pain catastrophizing in patients with chronic pain and alexithymia. Methods: This was a semi-experimental research with pre-test, post-test ,a two months' follow-up, and a control group. The statistical population included all the patients with chronic pain in Isfahan city in 2022. Sampling was done through convenience sampling method among the volunteer patients referred to private clinics in two stages. 24 patients with chronic pain who scored above 60 in Toronto alexithymia questionnaire (1994) were selected and were randomly assigned to experimental and control groups (n = 12). The instruments included Sullivan's pain catastrophizing scale questionnaire (1995) and Taylor and Cox's anxiety sensitivity questionnaire (1998. The experimental group received cognitive analytical therapy for 16 90-minute sessions once a week, and the control group did not receive any intervention. Data were analyzed using repeated measure and ANOVA analysis. Results: The results of data analysis showed that there was a significant difference between the adjusted mean of anxiety sensitivity and pain catastrophizing in the two groups. The mean scores of anxiety sensitivity and pain catastrophizing variables increased in post-test and follow-up phase (P<0/05). Therefore, it can be concluded that cognitive analytic therapy was effective for anxiety sensitivity and catastrophizing pain in patients with chronic pain and alexithymia (P<0/05). Conclusion: Considering the effect of cognitive analytic therapy on anxiety sensitivity and pain catastrophizing, this treatment method can be used to improve patients with chronic pain and alexithymia.
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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 | 0.000 | 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) | 0.001 | 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".