Predictors of Use of Complementary and Alternative Therapies Among Patients With Cancer
Bibliographic record
Abstract
PURPOSE/OBJECTIVES: To determine predictors of use of complementary and alternative medicine (CAM) therapies among patients with cancer. DESIGN: Secondary analysis of two federally funded panel studies. SETTING: Urban and rural communities in the midwestern United States. SAMPLE: Patients with lung, breast, colon, or prostate cancer (N = 968) were interviewed at two points in time. 97% received conventional cancer treatment, and 30% used CAM. The sample was divided evenly between men and women, who ranged in age from 28-98; the majority was older than 60. METHODS: Data from a patient self-administered questionnaire were used to determine CAM users. Responses indicated use of herbs and vitamins, spiritual healing, relaxation, massage, acupuncture, energy healing, hypnosis, therapeutic spas, lifestyle diets, audio or videotapes, medication wraps, and osteopathic, homeopathic, and chiropractic treatment. MAIN RESEARCH VARIABLES: Dependent variable for analysis was use or nonuse of any of the identified CAM therapies at time of interviews. Independent variables fell into the following categories: (a) predisposing (e.g., gender, age, race, education, marital status), (b) enabling (e.g., income, health insurance status, caregiver presence, geographic location), and (c) need (e.g., cancer stage, site, symptoms, treatment, perceived health need). FINDINGS: Significant predictors of CAM use were gender, marital status, cancer stage, cancer treatment, and number of severe symptoms experienced. CONCLUSIONS: Patients with cancer are using CAM while undergoing conventional cancer treatment. IMPLICATIONS FOR NURSING: Nurses need to assess for CAM use, advocate for protocols and guidelines for routine assessment, increase knowledge of CAM, and examine coordination of services between conventional medicine and CAM to maximize positive patient outcomes.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".