Treatment provision and management for the menopause: a multinational survey study
Bibliographic record
Abstract
Introduction Despite available safe hormonal and non-hormonal interventions, most women with troublesome menopausal symptoms do not receive effective, evidence-based therapy, with notable international disparities in provision. This study aimed to investigate self-reported menopausal care experiences in a self-selecting sample from five English-speaking countries: Australia, Canada, New Zealand, the United Kingdom, and the United States, through an anonymous online survey. Methods The 15–20 min survey, delivered via Qualtrics XM®, included questions on sociodemographic characteristics and treatment experiences, such as the number of healthcare professionals (HCP) seen before getting a prescription, ease of obtaining treatment, involvement in treatment discussions, appropriateness of treatment review and optimization, side effect tolerability, and overall satisfaction. Results Data from 3,062 respondents were analyzed: Australia (16.59%, n = 508), Canada (17.54%, n = 537), New Zealand (16.59%, n = 508), UK (24.00%, n = 735), and US (25.28%, n = 774). Significant international differences were observed in both healthcare access and prescribing patterns. More women in the UK and US consulted an HCP compared with Australia, Canada, and New Zealand [ χ ²(4, N = 3062) = 101.02, p < 0.001, φ c = 0.18]. Prescription rates were higher in New Zealand, the UK, and the US compared with Australia and Canada [ χ ²(4, N = 2,485) = 75.71, p < 0.001, φ c = 0.18]. However, UK respondents, despite longer treatment use, generally reported less involvement in treatment discussions, poorer treatment review, lower side effect tolerability, and reduced satisfaction compared with other countries across treatment types. Discussion Based on a self-selected cohort, these findings reveal critical gaps in menopause care, including disparities in treatment access and international differences in patient involvement. Greater access to healthcare in the UK and the US did not translate into higher satisfaction, highlighting the need for patient-centered approaches. Improving care requires better clinician education and strategies to enhance communication and shared decision-making.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".