Factors associated with complementary and alternative medicine use among women at midlife
Bibliographic record
Abstract
Hormone replacement therapy (HRT) has been established as the first-line treatment for women experiencing menopausal symptoms. The use of complementary and alternative medicine (CAM), however, is becoming increasingly popular among women at midlife for management of such symptoms. Despite the equivocal evidence of CAM's efficacy in the reduction and alleviation of menopausal symptoms in placebo-controlled, randomized trials, 50% of women at midlife use CAM. To date, several large, population-based studies have focused upon CAM use amongst menopausal women and the factors associated with the adoption of such therapies. By identifying women in the menopausal transition who tend to use CAM, this narrative review highlights evidence that aids women at this stage of life make better and individualized treatment choices to relieve these symptoms. The available evidence suggests that the prevalence of CAM use among menopausal women is high world-wide, but there is a paucity of high-quality studies that adequately assess the factors associated with its use. Further studies are needed to confirm the characteristics of women who employ CAM to manage their night sweats and hot flushes. Results of this study might enable the development of policies catering to the needs of those women and provide a resource to support their decision-making regarding treatment options.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".