Effects of Center-Based Delivery of Tai Chi and Qi Gong Group Classes on Self-Reported Symptoms in Cancer Patients and Caregivers
Bibliographic record
Abstract
Background: There is increasing interest in complementary approaches such as Tai Chi (TC) and Qi Gong (QG) in oncology settings. We explored the effects of TC/QG delivered in group classes at a comprehensive cancer center. Methods: Patients and caregivers who participated in TC or QG completed assessments before and after an in-person group class. Assessments included questions about expectancy/satisfaction and common cancer symptoms (Edmonton Symptom Assessment Scale [ESAS]). ESAS distress subscales analyzed included global (GDS), physical (PHS), and psychosocial (PSS). Results: Three hundred four participants (184 patients, 120 caregivers) were included in the analysis. At baseline, caregivers had a greater expectancy for change in energy level as a result of class participation compared with patients (22.9% vs 9.9%). No significant difference was observed between baseline patient and caregiver PSS. Clinically significant improvement in well-being was observed among patients in TC classes (1.0) and caregivers in QG classes (1.2). For fatigue, patients (1.4) and caregivers (1.0) participating in QG experienced clinically significant improvement. Both TC and QG classes were associated with clinically significant improvements (ESAS GDS decrease ≥3) in global distress for patients (TC = 4.52, SD= 7.6; QG = 6.05, SD = 7.9) and caregivers (TC = 3.73, SD = 6.3; QG = 4.02, SD = 7.8). Eighty-nine percent of participants responded that their expectations were met. Conclusions: Patients and caregivers participating in TC or QG group classes were satisfied overall and experienced significant improvement in global distress. Additional research is warranted to explore the integration of TC and QG in the delivery of supportive cancer care.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".