The impact of mindfulness-based interventions on symptom burden, positive psychological outcomes, and biomarkers in cancer patients
Bibliographic record
Abstract
Research on the use of mindfulness-based stress reduction and related mindfulness-based interventions (MBIs) in cancer care has proliferated over the past decade. MBIs have aimed to facilitate physical and emotional adjustment to life with cancer through the cultivation and practice of mindfulness (ie, purposeful, nonjudgmental, moment-to-moment awareness). This descriptive review highlights three categories of outcomes that have been evaluated in MBI research with cancer patients - namely, symptom reduction, positive psychological growth, and biological outcomes. We also examine the clinical relevance of each targeted outcome, while describing recently published original studies to highlight novel applications of MBIs tailored to individuals with cancer. Accumulating evidence suggests that participation in a MBI contributes to reductions in psychological distress, sleep disturbance, and fatigue, and promotes personal growth in areas such as quality of life and spirituality. MBIs may also influence markers of immune function, hypothalamic-pituitary-adrenal axis regulation, and autonomic nervous system activity, though it remains unclear whether these biological changes translate to clinically important health benefits. We conclude by discussing methodological limitations of the extant literature, and implications of matching MBIs to the needs and preferences of cancer patients. Overall, the growing popularity of MBIs in cancer care must be balanced against scientific evidence for their impact on specific clinical outcomes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".