Effectiveness of Mindfulness-Based Cognitive Therapy on Mental Pain, Distress Tolerance and Psychological Hardiness in Breast Cancer Patients
Bibliographic record
Abstract
The aim of this study was to determine the effectiveness of mindfulness therapy on mental pain, distress tolerance and psychological hardiness in breast cancer patients. The method of this study was quasi-experimental with pretest-posttest design and a one-month follow-up with control group. The statistical population consisted of all female patients referred to the medical clinics of Babol in 2022 who received definite diagnosis of breast cancer. Among these, 30 patients with breast cancer were selected by non-random sampling method and were randomly assigned to two experimental groups (mindfulness therapy) and control group (15 patients in each group). Mindfulness therapy was performed in 8 sessions of 90 minutes in the experimental group. Data were collected using Psychological Hardiness Questionnaire (2003), Distress Tolerance Scale (2005) and Mental Pain Inventory (2003). Data were analyzed using SPSS-22 software and repeated measure analysis of variance. The results showed that mindfulness therapy is effective on mental pain, distress tolerance and psychological hardiness in patients with breast cancer. It can be concluded that mindfulness therapy is effective on subjective pain, distress tolerance and psychological hardiness in patients with breast cancer and can be used to reduce the adverse effects of the disease on patients' lives and to take supportive measures
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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".