Investigating differences in cancer incidence between immigrants and non-immigrants: A population-based study from 1992-2015 using Statistics Canada data
Bibliographic record
Abstract
Cancer continues to be a contributing factor to Canada’s healthcare burden and has large impacts on quality of life and survival. While Canada has a public healthcare system, there are different challenges faced by those born outside of the country, and it is important to study any disparities in cancer outcomes to offer more equitable solutions. The research performed for this thesis aimed to investigate the differences in cancer incidence between immigrants and non-immigrants in Canada. Using Statistics Canada data and linking of the 1991 Census, Canadian Cancer Registry, and Canadian Vital Statistics database, a cohort was followed from 1992-2015 to calculate the odds ratios and hazard ratios of cancer incidence for immigrants and non-immigrants. The results support previous research in the field, specifically the existence of a healthy immigrant effect where immigrants have lower odds of developing cancer incidence in comparison to their Canadian-born counterparts. Results also show this effect decreases over time spent in the country, aligning with previous research. This study demonstrates the complexity of this phenomenon and suggests some factors that contribute to the healthy immigrant effect after controlling for various demographic and socioeconomic factors in the regression models. These results contribute to this area of research and also highlights the need for future studies to examine individual-level health behaviours and other factors that impact the differences in cancer incidence between immigrants and non-immigrants in Canada.,
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".