Bibliographic record
Abstract
Introduction: Cancer differentially affects populations and geographical regions. Given the ethnic diversity and growing population of immigrants in Canada and British Columbia in particular, it is important to understand how the risk of cancer is distributed according to where in BC immigrants live, given that this population may experience distinct cancer risks. Objectives: The purpose of this study is to understand how cancer incidence rates in BC vary by the regional proportion of immigrants and to explore how these rates are associated with duration of residence (recent versus well established), age at immigration, and country of origin. Methods: Analyses were conducted using a dataset of adult incident cancers diagnosed in BC (2000 to 2009) collected by the BC Cancer Registry. Regional-level estimates of the proportion of immigrants, as well as the socioeconomic and ethnic profiles of the BC population, were obtained from the Statistics Canada 2006 Census (defined by Local Health Area) and linked to the Cancer Registry data. Poisson and Negative Binomial regression models were used to estimate the rate ratios (RR) of cancer incidence by proportion of immigrants. Results: Overall, regional immigrant density significantly predicted lower cancer incidence rates for all-cancers and the most common cancers of the breast, prostate, colon and lung. However, for less common cancers of the liver, stomach and pharynx, proportion of immigrants significantly predicted higher cancer risk. This association was seen for recent and established immigrants, although cancer rates were higher among established immigrants. The proportion of immigrants at a younger age at immigration and from European origin were associated with increased risk for all-cancers and common cancers, but decreased risk of less common cancers. The proportion of immigrants at an older age at arrival (particularly 45 years and older) and from Asian origin were associated with decreased all-cancer risk and the risk of common cancers, but increased risk of less common cancers. Conclusion: Regional-level concentration of immigrants predicted cancer incidence rates in BC. Regional data on cancer incidence is important for developing effective health promotion strategies and public health planning by various Local Health Areas and health authorities in BC.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".