Efficacy of the Complementary and Alternative Therapies for the Management of Psychological Symptoms of Menopause: A Systematic Review of Randomized Controlled Trials
Bibliographic record
Abstract
Menopause is not a high-risk period for psychiatric illness but can cause psychological issues; the most common of which are anxiety and depression, which can impair coping and reduce women's quality of life. Thus, many women have leaned toward complementary and alternative medicine (CAM) for the relief of menopause-related symptoms. No rigorous study exists in the literature on the effects of CAMs on the psychological symptoms of menopause despite this growing patient interest. This systematic review aimed to assess the efficacy of CAM interventions on psychological symptoms of menopause. Databases (PubMed, Web of Science, Scopus, Cochrane Library, and Google Scholar) were searched from January 2000 to May 2021 using the keywords: menopause, menopausal symptoms, psychological symptoms, and complementary and alternative medicine. The quality of the included studies was assessed using the Mixed Methods Appraisal Tool (MMAT) for randomized clinical trials. Of the 704 articles found, 33 articles with 3,092 participants entered the final review. Aromatherapy, massage, yoga, and acupuncture, as well as some dietary and herbal supplements improved psychological symptoms during menopause based on the findings of the current study. However, the effectiveness of reflexology and exercise was debatable. However, necessary precautions should be taken when using them in clinical settings despite the positive effect of various CAM interventions on reducing psychological symptoms. More studies with a higher methodology quality are required to make better decisions about the effect of various CAM interventions on the psychological symptoms of menopause.
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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 | 0.021 | 0.075 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.015 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".