Low Rates of Cervical Cancer Screening Among Urban Immigrants
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 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 it