Use of Complementary/Alternative Medicine by Breast Cancer Survivors in Ontario: Prevalence and Perceptions
Bibliographic record
Abstract
PURPOSE: To determine the prevalence of use of complementary/alternative medicine (CAM) by breast cancer survivors in Ontario, Canada, and to compare the characteristics of CAM users and CAM nonusers. PATIENTS AND METHODS: A questionnaire was mailed to a random sample of Ontario women diagnosed with breast cancer in 1994 or 1995. RESULTS: The response rate was 76.3%. Overall, 66.7% of the respondents reported using CAM, most often in an attempt to boost the immune system. CAM practitioners (most commonly chiropractors, herbalists, acupuncturists, traditional Chinese medicine practitioners, and/or naturopathic practitioners) were visited by 39.4% of the respondents. In addition, 62.0% reported use of CAM products (most frequently vitamins/minerals, herbal medicines, green tea, special foods, and essiac). Almost one half of the respondents informed their physicians of their use of CAM. Multiple logistic regression analysis determined that support group attendance was the only factor significantly associated with CAM use. CONCLUSION: CAM use is common among Canadian breast cancer survivors, many of whom are discussing CAM therapy options with their physicians. Knowledge of CAM therapies is necessary for physicians and other health care practitioners to help patients make informed choices. CAM use may play a role in the positive benefits associated with support group attendance.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".