Potential effects of virtual interventions for menopause management: a systematic review
Bibliographic record
Abstract
IMPORTANCE: Menopausal women are one of the fastest growing demographic groups globally. Virtual interventions have emerged as alternate avenues for menopausal women to manage and cope with their symptoms. OBJECTIVE: The purpose of this review is to summarize existing research on the potential effects of virtual interventions for menopause management. EVIDENCE REVIEW: This systematic review was written in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. MEDLINE, PsychINFO, CINAHL, AgeLine, ERIC, ProQuest, Nursing and Allied Health Database, PsychARTICLES, and Sociology Database were used for literature search and searched from conception to December 2021. Original studies, including randomized controlled trials and quasi-experimental studies, were included if they evaluated a virtual intervention for menopause management and investigated the effects of these interventions on physical and psychosocial outcomes and/or the feasibility of these interventions among menopausal women. Included studies were published in peer-reviewed journals and assessed for quality using the Critical Appraisal Skills Program Checklists. FINDINGS: A total of 16 articles were included in this review. Virtual interventions have the potential to improve physical health outcomes including body weight/body mass index/waist circumference, pain, blood pressure, and cholesterol. However, conflicting results were identified for the outcomes of vasomotor and endocrine symptoms, sleep, and sexual functioning. Virtual interventions might also improve psychosocial outcomes, including knowledge and patient-physician communication, although conflicting results were again identified for treatment decision-making ability, quality of life, and anxiety and depression. Virtual interventions were feasible in terms of being usable and cost-effective, and eliciting satisfaction and compliance among menopausal women. CONCLUSIONS AND RELEVANCE: Virtual interventions might have the potential to improve the physical and psychosocial health outcomes of menopausal women, although some conflicting findings arose. Future studies should focus on including diverse menopausal women and ethnic minorities, conducting research within low- to middle-income countries and communities, further exploring intervention design to incorporate features that are age and culture sensitive, and conducting full randomized controlled trials to evaluate the effects of the interventions.
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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 | 0.010 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".