Low Rates of Cervical Cancer Screening Among Urban Immigrants
Bibliographic record
Abstract
OBJECTIVE: Women who are immigrants or socioeconomically disadvantaged have been found to have significantly lower cervical cancer screening rates than their peers in Toronto, Ontario, Canada. The objective of this study was to examine rates of appropriate cervical cancer screening among women living in Ontario, Canada, using recent registration with Ontario's universal health insurance plan as an indicator of immigrant status. METHODS: This retrospective cohort study included 2,273,995 screening-eligible women aged 25 to 69 years, who resided in Ontario's metropolitan areas during the calendar years 2003, 2004, and 2005. A validated algorithm was applied to the Ontario-wide physicians' claims database to determine which women had undergone cervical cancer screening with a Pap test during the 3-year period. RESULTS: Appropriate cervical cancer screening occurred for 61.1% of women. Despite adjustment for physician contact and pregnancy rates, cervical cancer screening rates were especially low among: women aged 50 to 69 years; women living in low-income areas; and women who had registered with Ontario's universal health insurance plan within the preceding 10 years, a group consisting largely of recent immigrants. Women with all 3 of these characteristics had a screening rate of 31.0% compared with 70.5% among women with none of these characteristics. CONCLUSION: Within a system of universal health insurance, appropriate cervical cancer screening is significantly lower among women who are older, living in low-income areas, or recent immigrants. Efforts to reduce disparities in cervical cancer screening should focus on women with these characteristics.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".