The Experience of Mindfulness-Based Stress Reduction on Menopausal Symptoms, Sleep Disturbance, and Body Image among Patients with Breast Cancer—A Qualitative Study
Bibliographic record
Abstract
BACKGROUND AND AIM: The objective was to decrease patient menopausal symptoms, sleep disturbance, and body image using a nonpharmacological therapy for cultivating key healthy lifestyle habits in patients with breast cancer. MATERIALS AND METHODS: The participants were 26 women with breast cancer who had recently received structured mindfulness-based stress reduction (MBSR) training in a clinical trial. Focus groups and interviews were conducted, during which the participants were asked semistructured, open-ended questions regarding the experiences of MBSR. RESULTS: The participants indicated that MBSR helped them to alleviate hot flashes and night sweats, and improve sleep quality and be more at ease with the external aspect of their body. On the other hand, during MBSR intervention in a group manner, the participants felt more psychological support and an outlet for sharing negative emotional experiences. CONCLUSION: This study identified the short-term benefits associated with group-based MBSR for women with breast cancer. In addition, our research identified the difficulties of intervention measures and coping methods. The study described the benefits of MBSR for patients with breast cancer. The findings of this study will help nursing staff identify the main coping menopausal symptoms and control negative mental health.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".