Yoga in Adult Cancer: A Pilot Survey of Attitudes and Beliefs among Oncologists
Bibliographic record
Abstract
BACKGROUND: Depending on interest, knowledge, and skills, oncologists are adapting clinical behaviour to include integrative approaches, supporting patients to make informed complementary care decisions. The present study sought to improve the knowledge base in three ways: Test the acceptability of a self-reported online survey for oncologists.Provide preliminary data collection concerning knowledge, attitudes, beliefs, and current referral practices among oncologists with respect to yoga in adult cancer.List the perceived benefits of and barriers to yoga intervention from a clinical perspective. METHODS: A 38-item self-report questionnaire was administered online to medical, radiation, and surgical oncologists in British Columbia. RESULTS: Some of the 29 oncologists who completed the survey (n = 10) reported having recommended yoga to patients to improve physical activity, fatigue, stress, insomnia, and muscle or joint stiffness. Other responding oncologists were hesitant or unlikely to suggest yoga for their patients because they had no knowledge of yoga as a therapy (n = 15) or believed that scientific evidence to support its use is lacking (n = 11). All 29 respondents would recommend that their patients participate in a clinical trial to test the efficacy of yoga. In qualitative findings, oncologists compared yoga with exercise and suggested that it might have similar psychological and physical health benefits that would improve patient capacity to endure treatment. Barriers to and limitations of yoga in adult cancer are also discussed. CONCLUSIONS: An online self-report survey is feasible, but has response rate limitations. A small number of oncologists are currently recommending yoga to improve health-related outcomes in adult cancer. Respondents would support clinical yoga interventions to improve the evidence base in cancer patients, including men and women in all tumour groups.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".