Prevalence of Hysterectomy by Self-Reported Disability Among Canadian Women: Findings from a National Cross-Sectional Survey
Bibliographic record
Abstract
Introduction: Our objective was to investigate differences in prevalence of hysterectomy by self-reported disability status among Canadian women. Materials and Methods: We analyzed cross-sectional data from the Canadian Community Health Survey 2012 on 30,170 women aged ≥20 years. Disability was defined as reports of sometimes or often (vs. never) experiencing functional limitations or reduction in daily activities at home, school, or work. Frequency of these limitations was used as a proxy for disability severity. The outcome was self-reported hysterectomy status. Modified Poisson regression was used to quantify the prevalence ratio (PR) and 95% confidence interval (CI) for hysterectomy according to any, functional, or activity-limiting disability, after adjustment for household income, employment, education, ethnicity, and marital status. Results were stratified by age at time of data collection, categorized as childbearing (20–44 years), perimenopausal (45–59 years), and postmenopausal (60 years and older). Results: Disability was significantly and consistently associated with higher prevalence of hysterectomy in women. The strength of association was inversely related to age category, and PRs for a given age category were similar across disability types and severity levels. PRs for the association between any disability and hysterectomy were 2.18 (95% CI 1.36–3.50) for childbearing-aged women, 1.48 (95% CI 1.21–1.80) for perimenopausal women, and 1.12 (95% CI 1.02–1.24) for postmenopausal women. Conclusions: Prevalence of hysterectomy is disproportionately higher among women with self-reported disabilities compared with women without disabilities, with these differences most pronounced in women of childbearing age.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".