Cervical and Breast Cancer Screening After CARES: A Community Program for Immigrant and Marginalized Women
Bibliographic record
Abstract
INTRODUCTION: Marginalized populations such as immigrants and refugees are less likely to receive cancer screening. Cancer Awareness: Ready for Education and Screening (CARES), a multifaceted community-based program in Toronto, Canada, aimed to improve breast and cervical screening among marginalized women. This matched cohort study assessed the impact of CARES on cervical and mammography screening among under-screened/never screened (UNS) attendees. METHODS: Provincial administrative data collected from 1998 to 2014 and provided in 2015 were used to match CARES participants who were age eligible for screening to three controls matched for age, geography, and pre-education screening status. Dates of post-education Pap and mammography screening up to June 30, 2014 were determined. Analysis in 2016 compared screening uptake and time to screening for UNS participants and controls. RESULTS: From May 15, 2012 to October 31, 2013, a total of 1,993 women attended 145 educational sessions provided in 20 languages. Thirty-five percent (118/331) and 48% (99/206) of CARES participants who were age eligible for Pap and mammography, respectively, were UNS on the education date. Subsequently, 26% and 36% had Pap and mammography, respectively, versus 9% and 14% of UNS controls. ORs for screening within 8 months of follow-up among UNS CARES participants versus their matched controls were 5.1 (95% CI=2.4, 10.9) for Pap and 4.2 (95%=CI 2.3, 7.8) for mammography. Hazard ratios for Pap and mammography were 3.6 (95% CI=2.1, 6.1) and 3.2 (95% CI=2.0, 5.3), respectively. CONCLUSIONS: CARES' multifaceted intervention was successful in increasing Pap and mammography screening in this multiethnic under-screened population.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".