Education level is associated with the occurrence and timing of hysterectomy: A cohort study of Canadian women
Bibliographic record
Abstract
INTRODUCTION: Hysterectomy is a common surgery with discernible practice variations that could be influenced by socioeconomic factors. We examined the association between level of educational attainment and the occurrence and timing of hysterectomy in Canadian women. MATERIAL AND METHODS: We conducted a prospective cohort study of 30 496 females in the Alberta's Tomorrow Project (2000-2015) followed approximately every 4 years using self-report questionnaires. Educational attainment was defined as high school diploma or less, college degree, university degree (reference group), and postgraduate degree. We used logistic regression analyzing hysterectomy occurrence at any time and before menopause, separately, and flexible parametric survival models analyzing hysterectomy timing with age as the time scale. Multivariable models controlled for race/ethnicity, rural/urban residence, parity, oral contraceptive use, and smoking. RESULTS: Overall, 39.1% of females reported a high school diploma or less, 28.9% reported a college degree, 23.5% reported a university degree, and 8.5% reported a postgraduate degree. A graded association was observed between lower education and higher odds of hysterectomy (high school or less: adjusted odds ratio [AOR] 1.68, 95% CI 1.55-1.82; college degree: AOR 1.58, 95% CI 1.45-1.72); results were similar for premenopausal hysterectomy. A graded association between lower education and earlier timing of hysterectomy was also observed up to approximately age 60 (eg at age 40: high school or less adjusted hazard ratio [AHR] 1.61, 95% CI 1.49-1.75; college degree AHR 1.53, 95% CI 1.40-1.67). CONCLUSIONS: Women with lower levels of education were more likely to experience hysterectomy, including hysterectomy before menopause and at younger ages.
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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 | 0.001 | 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 | 0.001 |
| 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".