Ethnic and racialized disparities in the use of screening services for pap smears and mammograms in Canada
Bibliographic record
Abstract
BACKGROUND: Breast and cervical cancers pose significant health challenges for women globally, emphasizing the critical importance of effective screening programs for early detection. In Canada, despite the implementation of accessible healthcare systems, ethnic and racialized disparities in cancer screening persist. This study aims to assess ethnic and racialized disparities in breast and cervical cancer screening in Canada. METHODS: Using 2015-2019 data from the Canadian Community Health Survey, we analyzed women aged 18-70 in distinct ethnic and racial groups. The primary outcome was mammography or Papanicolaou test (pap smear). The secondary outcome was time since the last screening. We used weighted multivariable logistic regression to estimate the odds of having a pap smear or mammography across the ethnic and racialized groups, adjusted for relevant covariates. Results were reported as odds ratios (ORs) with 95% confidence intervals (CIs). RESULTS: We included 14,628,067 women of which 72.5% were White, 8.4% Southeast Asian, 4.7% South Asian, 3.4% Indigenous, 2.7% Black, 2.0% West Asian, and 1.6% Latin American. In comparison with the White reference group, a higher odds ratio of not having a pap smear was estimated for the West Asian (5.63; CI 3.85, 8.23), South Asian (5.19; CI 3.79, 7.12), Southeast Asian (4.35; CI 3.46, 5.46), and Black groups (2.62; CI 1.82, 3.78). Disparities in mammography screening were found only for the Southeast Asian group with higher odds of not having screening (1.85; CI 1.15, 2.98) compared to the White reference group. CONCLUSION: This study reveals significant disparities in pap smear and mammography screenings affecting various ethnic groups, particularly in West Asia, South Asian, and Black communities. These findings underscore the urgent need for targeted interventions, policies, and healthcare strategies to address these gaps and ensure equitable access to essential breast and cervical cancer prevention across all ethnicity.
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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 | 0.005 |
| 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) | 0.003 | 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".