The Use of Complementary and Alternative Medicines Among a Sample of Canadian Menopausal‐Aged Women
Bibliographic record
Abstract
INTRODUCTION: Despite questionable efficacy and safety, many women use a variety of complementary and alternative medicine (CAM) therapies to relieve menopause symptoms. METHODS: We examined the determinants and use of CAM therapies among a sample of menopausal-aged women in Canada by using a cross-sectional Web-based survey. RESULTS: Four hundred twenty-three women who were contacted through list serves, e-mail lists, and Internet advertisements provided complete data on demographics, use of CAM, therapies, and menopausal status and symptoms. Ninety-one percent of women reported trying CAM therapies for their symptoms. Women reported using an average of five kinds of CAM therapies. The most common treatments were vitamins (61.5%), relaxation techniques (57.0%), yoga/meditation (37.6%), soy products (37.4%), and prayer (35.7%). The most beneficial CAM therapies reported were prayer/spiritual healing, relaxation techniques, counseling/therapy, and therapeutic touch/Reiki. Demographic factors and menopausal symptoms contributed to 14% of the variance (P < .001) in the number of CAM therapies tried. DISCUSSION: Results support previous research showing that menopausal women have high user rates of CAM therapy and show that specific demographic factors and somatic symptomatology relate to use of CAM therapies. Health care providers can benefit from understanding the determinants and use of CAM by women during the menopause transition if they are to help and provide quality care for this population.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".