Reasons for and Characteristics Associated With Complementary and Alternative Medicine Use Among Adult Cancer Patients: A Systematic Review
Bibliographic record
Abstract
PURPOSE: To conduct a systematic review of reasons for and sociodemographic and disease characteristics associated with complementary and alternative medicine (CAM) use in cancer patients. METHODS: Eligible studies were identified by searching the following databases: Alt Health Watch, AMED, CINAHL, CancerLit, PremMEDLINE, MEDLINE, Pub-Med, Ingenta, EMBASE, and Health Star, as well as reference lists in review articles. Only English-language articles published between 1994 and 2004 were included. Search terms included CAM and oncology/cancer, decision making and CAM and oncology/cancer, treatment decision making and CAM and oncology/cancer, and health care choices and CAM and oncology/cancer. RESULTS: Fifty-two eligible studies were identified and summarized. These studies were conducted in 14 different countries, with the largest number of studies being completed in the United States (34.6%). A therapeutic response, wanting control, a strong belief in CAM, CAM as a last resort, and finding hope were the most commonly cited reasons for using CAM. Age, socioeconomic status, and gender were the dominant characteristics associated with CAM use. CONCLUSION: Reasons for and characteristics associated with CAM use among cancer patients have been studied extensively. Future CAM research among cancer patients should focus on identifying decision-making processes and building theoretical decision-making models. These can be used in the development of decisional aids for patients when confronted with the choice to use CAM as part of their cancer treatment.
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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 | 0.007 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".