Can Mindfulness-Based Stress Reduction Influence the Quality of Life, Anxiety, and Depression of Women Diagnosed with Breast Cancer? —A Review
Bibliographic record
Abstract
INTRODUCTION: Breast cancer is the most common form of cancerous disease worldwide. Its treatment leads to a variety of physiological and psychological side effects. This review investigates the question of how mindfulness-based stress reduction (MBSR), a stress management program, can influence the quality of life, anxiety, and depression of women diagnosed with breast cancer. METHODS: A systematic literature search was conducted in PubMed/MEDLINE and Cochrane Library. Screening by title, abstract and full text was performed, whereby only those articles were included that fit the inclusion criteria. A risk of bias assessment was performed for each included study. RESULTS: Overall, six studies were included, but not every study investigated all three outcomes. Two studies found positive impacts on quality of life, whereas three did not find a positive correlation between the intervention and quality of life. Four out of six studies found a positive relation between MBSR and anxiety scores, but only half of the included studies found positive results for the interaction between MBSR and depression scores. CONCLUSION: Published data suggest that anxiety can be positively influenced by MBSR, which can be used to improve the psychological care of breast cancer patients, both during and after treatment. However, further studies with larger patient numbers and longer observation periods should be conducted in order to elucidate the full potential of MSBR on important areas such as depression and quality of life.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".