Lifetime Cervical Cancer Screening and Social Determinants of Health in the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
OBJECTIVES: In Canada, cervical cancer rates remain well above the World Health Organization target, despite screening and vaccination programs. Modeling reveals that those who have never undergone screening represent one of the highest risk populations. The Canadian Longitudinal Study on Aging (CLSA) prospectively collected health outcomes on >50,000 individuals. The authors sought to identify the prevalence of Canadian female participants having never undergone cervical cancer screening and the association with social determinants of health. METHODS: The authors performed a cross-sectional analysis from CLSA data. The main outcome was self-report of ever having undergone a Pap smear. Regression analyses evaluated the association between demographic or social determinants of health and self-reported lifetime cervical cancer screening. RESULTS: The population-based sample comprised 22,910 participants aged 45-85, of whom 99.8% had available information on cervical cancer screening ( n = 22,720). The prevalence of never having undergone a Pap smear was 14.1%; weighted prevalence, 11.8% (95% CI = 11.0-12.6). The following factors were associated with never having undergone screening: older age (10-year) (OR = 1.5, 95% CI = 1.4-1.6), lower education (low vs. high) (OR = 1.5, 95% CI = 1.2-1.9), lower household income (low vs. high) (OR = 1.7, 95% CI = 1.3-2.3), having a religious affiliation (OR = 1.3, 95% CI = 1.1-1.5), and never being married/lived in common law (OR = 1.5, 95% CI = 1.2-1.9). Notably, not having a family physician was also associated (OR = 2.3, 95% CI = 1.6-3.3). However, among participants who never underwent a Pap smear, 97% reported having a family physician. CONCLUSIONS: This analysis highlights inequities in cervical cancer screening in the Canadian context. These insights are critical in informing a more equitable approach to implementing human papillomavirus (HPV)-based screening.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".