Women's mid-life health experiences in urban UK: an international comparison
Bibliographic record
Abstract
OBJECTIVES: First, to investigate cross-cultural variations in symptom prevalence for mid-life women using data from studies undertaken in the UK, Japan, China, Canada and the USA, and, second, to examine the relationship between symptoms, ethnicity, age and menopausal status for London's multi-ethnic urban women aged 45-55 years. METHODS: Cross-sectional, self-administered, postal questionnaire of women aged 45-55 years in London, UK (n = 1115), recruited from general practitioner lists. Participants recalled 15 general symptoms and the prevalence rates were compared with those of cohorts from methodologically similar studies. RESULTS: London women experienced high levels of general symptom reporting. Tiredness was the most prevalent symptom (65%) followed by aches or stiffness in the joints (54%). The prevalence of seven symptoms varied by menopausal group. Only the symptom of hot flushes varied by age. Tiredness, insomnia and irritability varied by ethnic group. The pattern of symptom reporting for the London cohort was more similar to the pattern of women in Beijing than to the pattern of cohorts in Manitoba, Massachusetts and Japan. CONCLUSIONS: Our data do not support the existence of a single menopausal syndrome. There appears to be dialectic between culture and biology. It can be argued that symptoms experienced during the menopausal transition arise through a complexity of factors, not simply declining levels of estrogen or ethnicity; geographic location, local culture and temporality are factors that also need to be taken into account.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".