Breast screening participation and retention among immigrants and nonimmigrants in British Columbia: A population‐based study
Bibliographic record
Abstract
Breast cancer screening programs operate across Canada providing mammography to women in target age groups with the goal of reducing breast cancer mortality through early detection of tumors. Disparities in breast screening participation among socio-demographic groups, including immigrants, have been reported in Canada. Our objectives were to: (1) assess breast screening participation and retention among immigrant and nonimmigrant women in British Columbia (BC), Canada; and (2) to characterize factors associated with screening among screening-age recent immigrant women in BC. We examined 2 population-based cohorts of women eligible for breast screening participation (537 783 women) and retention (281 052 women) using linked health and immigration data. Breast screening rates were presented according to socio-demographic and health-related variables stratified by birth country. Factors associated with screening among recent immigrant women were explored using Poisson regression. We observed marked variation in screening participation across birth country cohorts. Eastern European/Central Asian women showed low participation (37.9%) with rates from individual countries ranging from 35.0% to 49.0%. Participation rates for immigrant women from the most common birth countries, such as China/Macau/Hong Kong/Taiwan (45.7%), India (44.5%), the Philippines (45.9%), and South Korea (39.0%), were lower than the nonimmigrant rates (51.2%). Retention rates showed less variation by birth country; however, some disparities between immigrant and nonimmigrant groups persisted. Associations between screening indicators and study factors varied considerably across immigrant groups. Primary care physician visits were consistently positively associated with screening participation; this variable was also the only predictor associated with screening within each of the groups of recent immigrants. Our study provides unique data on both screening participation and retention among Canadian immigrant women compiled by individual country of birth. Our results are further demonstration that screening disparities exist among immigrant populations as well as in comparison with nonimmigrant women.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".