Exercise and Mindfulness-Based-Stress-Reduction: A Multidimensional Approach Towards Cancer Survivorship Care
Bibliographic record
Abstract
Cancer survivors often experience a variety of physiological deficits resulting from cancer treatment such as reduced muscle strength, decreased range of motion and poor balance. Cancer survivors also commonly experience psychosocial side effects, such as anxiety, depression and fear of recurrence. Overall, it is common for cancer survivors to report a decrease in physical and emotional wellbeing and overall quality of life. Research suggests that improvements in physical health can be achieved through moderate intensity exercise such as light resistance training and moderate aerobic exercise in this population. Mindfulness-Based-Stress-Reduction (MBSR) programming utilizes various mind/body techniques that can reduce state anxiety levels, distress and depression. While cancer survivors face numerous physiological and psychological challenges, exercise interventions focus on physical health, while MBSR interventions focus on psychosocial health. The American Medical Association (AMA) recommends a patient’s care should include psychological, physiological, psychosocial and educational components, emphasizing the need for an integrated approach to cancer survivorship. Integrating exercise and MBSR interventions may serve to optimize the overall health and quality of life of a cancer survivor.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".