Psychological Treatment for Pain Among Cancer Patients by Rational-Emotive Behavior Therapy - Efficacy in both India and Iran
Bibliographic record
Abstract
The aim of the present study is to find out the influence of rational-emotive behavior therapy (REBT) on pain intensity among cancer patients in India and Iran. The study followed a quasi-experimental, pre-post test, carried out with a sample of 88 cancer patients, aged 21-52 years, referred to the Baharat cancer hospital of Mysore in India and Shahidzade hospital of Behbahan in Iran. They were randomly assigned to the experimental (n=India 21; Iran 22) and control (n=India 22; Iran 23) groups. Pain was measured with the McGill Pain Questionnaire- MPQ (1975), the intervention by REBT has given to the experimental group for 45 days (ten sessions) and at the end of intervention, the pain of patients was again evaluated. Concerning to hypothesis of the study, two independent sample T test and three ways mixed ANOVA is used to analyze the data. Results showed that the experimental group in post test had less pain than the control group, but there were no statistically significant differences between Indian and Iranian patients in pain perception. With respect the outcome of study, it has realized that REBT can be used in hospitals and other psychological clinics to reduce the pain of cancer patients.
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 | 0.001 |
| 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".