Comparing the Efficacy of CBT and ACT on Pain Perception in Patients With Leukemia
Bibliographic record
Abstract
Background and Aim: This study aimed to assess and compare the effectiveness of cognitive behavioral therapy (CBT) and acceptance and commitment therapy (ACT) on pain perception in patients with leukemia. Materials and Methods: This quasi-experimental study employed a pre-test/post-test-follow-up approach with a control group. The sample consisted of 45 patients with chronic leukemia, selected via convenience sampling from Shahid Rajaei Hospital in Karaj City, Iran (2024), and randomly assigned to experimental and control groups. Data from the McGill pain questionnaire (MPQ) were analyzed across all study phases using multivariate analysis of variance (MANOVA) and multivariate analysis of covariance (MANCOVA), with a significance level of P<0.05, utilizing SPSS software, version 26. Results: This study found that both CBT and ACT led to significant improvements in pain perception compared to the control group. Univariate analyses showed that both interventions had a meaningful impact on the sensory, affective, and miscellaneous dimensions of pain, while no significant changes were found in evaluative pain perception (CBT: F=7.401, P<0.001, effect size=0.687; ACT: F=25.94, P<0.001, effect size=0.885). Repeated-measures ANOVA confirmed these improvements at both the post-test and follow-up stages. Notably, ACT was more effective than CBT in reducing overall pain perception and affective pain responses (ACT: F=25.94, P<0.001, effect size=0.885; CBT: F=7.401, P<0.001, effect size=0.687). Conclusion: The study found that both CBT and ACT effectively improved pain perceptions in patients with leukemia, with ACT showing greater effectiveness than CBT.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
| 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".