Patient satisfaction and experiences with menopause care for people with autoimmune diseases: an international mixed-methods study from the Menopause MATTERs Project
Bibliographic record
Abstract
OBJECTIVES: To understand the experiences and satisfaction with menopause care for women with autoimmune diseases. STUDY DESIGN: Exploratory, mixed-methods study (between December 2024 and March 2025) using an online survey for peri-, menopausal, and postmenopausal individuals (≥18 years), with and without confirmed autoimmune diagnoses. Survey participants were purposively selected for semi-structured interviews. MAIN OUTCOME MEASURES: Satisfaction with menopause care as measured across nine (co-designed) domains of: availability and access to clinicians, clinicians' knowledge, involvement in decision-making, consideration of primary disease, clinicians' empathy for physical and mental health symptoms, continuity and follow-up support, information received, flexibility in treatment. Other outcomes included qualitative themes from interviews with patients, types of clinicians consulted for menopause and reasons for seeking private menopause care and process measures of access to care. RESULTS: Satisfaction was significantly lower amongst women with autoimmune diseases (n = 3754) than those without autoimmune diseases (n = 480) across the nine metrics studied (p < 0·001). Qualitative analysis identified three themes: (1) menopause care was reactive and dependent on patients advocating for themselves; (2) there was fragmented and siloed care between specialties, with limited integration of the intersection between autoimmune diseases and menopause; and (3) mental health concerns often overshadowed menopause and autoimmune disease symptoms. CONCLUSION: The menopausal transition must be recognised as a unique stage in the management of autoimmune diseases. Our study suggests that menopause advice and care would benefit from increased clinician proactivity, empathy and knowledge. Greater evidence to inform clinical guidance and interdisciplinary training and integration is required.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".