Predictors of Use of Complementary and Alternative Therapies Among Patients With Cancer
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 it