Group Yoga Effects on Cancer Patient and Caregiver Symptom Distress: Assessment of Self-reported Symptoms at a Comprehensive Cancer Center
Bibliographic record
Abstract
BACKGROUND: Complementary and integrative health approaches such as yoga provide support for psychosocial health. We explored the effects of group-based yoga classes offered through an integrative medicine center at a comprehensive cancer center. METHODS: Patients and caregivers had access to two yoga group classes: a lower intensity (YLow) or higher intensity (YHigh) class. Participants completed the Edmonton Symptom Assessment System (ESAS; scale 0-10, 10 most severe) immediately before and after the class. ESAS subscales analyzed included global (GDS; score 0-90), physical (PHS; 0-60), and psychological distress (PSS; 0-20). Data were analyzed examining pre-yoga and post-yoga symptom scores using paired t-tests and between types of classes using ANOVAs. RESULTS: From July 18, 2016, to August 8, 2017, 282 unique participants (205 patients, 77 caregivers; 85% female; ages 20-79 years) attended one or more yoga groups (mean 2.3). For all participants, we observed clinically significant reduction/improvement in GDS, PHS, and PSS scores and in symptoms (ESAS decrease ≥1; means) of anxiety, fatigue, well-being, depression, appetite, drowsiness, and sleep. Clinically significant improvement for both patients and caregivers was observed for anxiety, depression, fatigue, well-being, and all ESAS subscales. Comparing yoga groups, YLow contributed to greater improvement in sleep versus YHigh (-1.33 vs -0.50, P = .054). Improvement in fatigue for YLow was the greatest mean change (YLow -2.12). CONCLUSION: A single yoga group class resulted in clinically meaningful improvement of multiple self-reported symptoms. Further research is needed to better understand how yoga class content, intensity, and duration can affect 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".