Reasons to Use and Disclose Use of Complementary Medicine Use – An Insight from Cancer Patients
Bibliographic record
Abstract
Studies have shown a high prevalence (40-83%) of complementary and alternative medicine (CAM) use among cancer patients in the U.S.A cross-sectional, mixed-methods pilot study was completed. This paper focuses on the quantitative analysis conducted on demographic predictors of complementary medicine (CM) use, reasons to use CM, and disclosure to healthcare provider data. Surveys were interview-administered at the Loma Linda University Medical Center Cancer Center. Participants, 18 years or older, were selected from a convenient sample. Eighty-seven percent (87.9%) of participants reported to have used CM as a cancer treatment and most reported to have used it "to help fight the cancer." Women were eight-times more likely to use prayer. All non-Caucasian and Hispanic participants reported to use CM as a cancer therapy and none reported to use a CM provider. More women (72%) disclosed their CM use than men (53.3%). Different prevalences and predictors exist when differentiating CM modalities, reasons to use CM vary by gender, and disclosure proportions vary by gender.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".