Weathering Storms: A Cohort Study of How Participation in a Mindfulness-Based Stress Reduction Program Benefits Women after Breast Cancer Treatment
Bibliographic record
Abstract
INTRODUCTION: A growing number of psychosocial interventions are being offered to cancer patients during and after their medical treatment. Here, we examined whether Mindfulness-Based Stress Reduction (MBSR), a stress management course, helps women to cope better with stress and illness once their breast cancer treatment is completed. Our aim was to understand how MBSR may benefit those who participate in the course. METHODS: Our cohort study enrolled 59 women in an 8-week MBSR program. They completed "before and after" questionnaires pertaining to outcomes (stress, depression, medical symptoms) and process variables (mindfulness, coping with illness, sense of coherence). Paired t-tests examined changes from before to after the MBSR course. Changes in mindfulness were correlated with changes in post-MBSR variables, and a regression analysis examined which variables contributed to a reduction in stress after program participation. RESULTS: Adherence to the program was 91%. Participants reported significant reductions in stress (p < 0.0001), depression (p < 0.0001), and medical symptoms (p < 0.0001), and significant improvements in mindfulness (p < 0.0001), coping with illness (p < 0.0001), and sense of coherence (p < 0.0001). Changes in mindfulness were significantly related to changes in depression, stress, emotional coping, and sense of coherence. Increases in mindfulness and sense of coherence predicted reductions in stress. CONCLUSIONS: It appears that learning how to be mindful is beneficial for women after their treatment for breast cancer.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".