COMPLEMENTARY AND ALTERNATIVE MEDICINE: USE IN AN OLDER POPULATION
Bibliographic record
Abstract
The aging North American population validates increased research of complementary and alternative medicine (CAM) use by older adults. The purpose of this study was to examine older adults' attitudes and motivations toward CAM use in an attempt to explain its limited usage. Senior citizens (66 to 100 years) were qualitatively surveyed and interviewed to analyze trends in CAM use. Forty-two participants older than 65 completed a questionnaire and 10 of those same individuals participated in an interview session. Motivations for CAM use, prevalence of CAM use, knowledge of CAM, and physician attitudes were investigated. The results of the survey and interviews showed older adults' most prevalent motivations for using CAM were pain relief (54.8%), improved quality of life (45.2%), and maintenance of health and fitness (40.5%). Knowledge of CAM was extremely low across the entire sample, but a significant difference in knowledge level existed among CAM users and nonusers. The CAM therapies most commonly used by older adults were chiropractic (61.9%), herbal medicine (54.8%), massage therapy (35.7%), and acupuncture (33.3%). This sample of senior citizens perceived CAM treatments to be extremely beneficial. Increased education about CAM is needed for older adults and health professionals. Practitioners of CAM should try to understand older adults' motivations for using CAM therapies and be involved in educating older adults about CAM.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".