Cancer and Complementary Therapies: Current Trends in Survivors’ Interest and Use
Bibliographic record
Abstract
BACKGROUND: Cancer survivors use complementary therapies (CTs) for a variety of reasons; however, with interest and use reportedly on the rise and a widening range of products and practices available, there is a need to establish trends in and drivers of interest. We aimed to determine (1) frequencies of use, level of interest, and barriers for 30 specific CTs and (2) whether physical symptoms, perceived stress (PS), or spiritual well-being were related to interest levels. METHOD: A total of 212 cancer outpatients were surveyed at the Tom Baker Cancer Centre in Calgary, Canada. RESULTS: Overall, up to 75% of survivors already used some form of CTs since their diagnosis. The most highly used were the following: vitamins B12 and D, multivitamins, calcium, and breathing and relaxation exercises. Those who had not used CTs indicated highest interest in massage, vitamin B12, breathing and relaxation, mindfulness-based stress reduction, and antioxidants. The most frequently reported barriers for all CTs were not knowing enough about what a therapy was and not having enough evidence on whether it worked. High PS predicted higher interest for all CTs, but spirituality was not significantly related to any. Physical symptoms, anxiety, and depression were significant predictors of interest for some CTs. CONCLUSION: These findings provide a blueprint for future clinical efficacy trials and highlight the need for clinical practice guidelines.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".