Cervical Cancer Screening among Women from Muslim-Majority Countries in Ontario, Canada
Bibliographic record
Abstract
Abstract Background: Immigrant women are less likely to be screened for cervical cancer in Ontario. Religion may play a role for some women. In this population-based retrospective cohort study, we used country of birth as a proxy for religious affiliation and examined screening uptake among foreign-born women from Muslim-majority versus other countries, stratified by region of origin. Methods: We linked provincial databases and identified all women eligible for cervical cancer screening between April 1, 2012, and March 31, 2015. Women were classified into regions based on country of birth. Countries were classified as Muslim-majority or not. Results: Being born in a Muslim-majority country was significantly associated with lower likelihood of being up-to-date on Pap testing, after adjustment for region of origin, neighborhood income, and primary care–related factors [adjusted relative risk (ARR), 0.93; 95% (confidence interval) CI, 0.92–0.93]. Sub-Saharan African women from Muslim-majority countries had the highest prevalence of being overdue (59.6%), and the lowest ARR for screening when compared with women from non–Muslim-majority Sub-Saharan African countries (ARR, 0.77; 95% CI, 0.76–0.79). ARRs were lowest for women with no primary care versus those in a capitation-based model (ARR, 0.28; 95% CI, 0.27–0.29 overall). Conclusions: We have shown that being born in a Muslim-majority country is associated with a decreased likelihood of being up-to-date on cervical screening in Ontario and that access to primary care has a sizeable impact on screening uptake. Impact: Screening efforts need to take into account the background characteristics of population subgroups and to focus on increasing primary care access for all. Cancer Epidemiol Biomarkers Prev; 26(10); 1493–9. ©2017 AACR.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".