Ethnicity, Immigration and Cancer Screening: Evidence for Canadian Women
Bibliographic record
Abstract
Introduction: Canada's annual immigrant intake is increasingly composed of visible minorities, with 59% of immigrants arriving in 1996-01 coming from Asia. However, only a small number of studies have used population health surveys to examine Canadian women's use of cancer screening. We use recent population health surveys to analyze immigrant and native-born women's use of Pap smears, breast exams, breast self-exams, and mammograms. Methods: We study women aged 21-65 drawn from the National Population Health Survey and Canadian Community Health Surveys that together yield a sample size of 105, 000 observations. Results: We find that for most forms of cancer screening, recent immigrants have markedly lower utilization rates, but these rates slowly increase with years in Canada. However, there is wide variation in rates of cancer screening by ethnicity. Screening rates for white immigrants approach Canadian-born women's utilization rates after 15-20 years in Canada, but screening rates for immigrants from Asia remain significantly below native-born Canadian levels. Discussion: Health authorities need to tailor their message about the importance of these forms of cancer screening to reflect the perceptions and beliefs of particular minority groups if the objective of universal use of preventative cancer screening is to be achieved.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".