Use of Complementary and Alternative Medicine by Cancer Patients at a Montreal Hospital
Bibliographic record
Abstract
OBJECTIVES: To assess feasibility of methods for a future study of complementary and alternative medicine (CAM) use by cancer patients treated in conventional health care settings. METHODS: Patients aged 18 years and older, fluent in English or French, and diagnosed with cancer from St. Mary's Hospital Center, Montreal, Canada participated. Feasibility was measured by the rates of participation and CAM use in the past 1 and 12 months. Following the survey, one patient focus group was held to better understand cancer patient perspectives on discussions of CAM that occur or not with their family physicians. RESULTS: Of 103 patients approached, 100 (97.1%; 77% female, 87% white) participated. Overall, 86% and 91% of respondents used at least one CAM in the past 1 and 12 months, respectively. More patients with breast compared with colorectal and other cancers (90.2%, 86.2%, and 80%, respectively) used CAM in the previous year. In the past 1 and 12 months, natural health products were used by 70% and 80% of respondents, respectively; mind-body therapies by 61% and 64%, respectively, and CAM practitioners by 11% and 29%, respectively. More than 98% of patients used CAM to improve quality of life and 68% disclosed CAM use to their physicians. Four of 5 focus group participants used CAM. Patient-physician CAM discussions varied from receiving a CAM referral to complete dismissal of the topic. CONCLUSION: Recruitment methods were well accepted but a sampling strategy stratified by sex and ethnicity will ensure sufficient representation by males and non-whites. Whereas disclosure of natural health products use is occurring, informative CAM discussion is not.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".