Public Health Evaluation:Avoidance of Breast Cancer Screening in Immigrant Canadians
Bibliographic record
Abstract
Breast cancer is one of the leading causes of death in Canadian women.1This cancer can be detected early through screening and allow for higher chances of survival. However, the Canadian Cancer Statistics Advisory Committee found that it is still being diagnosed at late stages, even with organized screening programs implemented in Canadian provinces.1 In Ontario, mammography is recommended every two years for women ages 50-69 years where they receive a medical referral letter invitation, but women are still found to present in clinics with no history of screening or advanced cancer.2 In 2017, 26500 breast cancer cases were found and in that 5000 women did not survive, with a majority of these women being immigrant. Canada is a multicultural country where more than 20% of the populations are immigrants, but yet more immigrant women die from breast cancer than non-immigrant women.3,4 However, screening participation rates remain lower in immigrants than non-immigrants,5 possibly being fatal. It’s important to know the causes of screening avoidance. Thus, the purpose of this literature review is to investigate the avoidance of breast cancer screening of immigrant women in Canada. Northern International Medical College Journal Vol. 13 No. 1-2 July 2021-January 2022, Page 566-567
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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 | 0.009 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".