Cervical cancer screening in Montreal: Building evidence to support primary care and policy interventions
Bibliographic record
Abstract
In Canada, over 40% of invasive cervical cancers occur among women who have never been screened. Although 12% of Canadian women have never been screened, this number can be as high as 43% among certain social groups. Little is published on factors associated with screening uptake and inequalities among women residing in Quebec. Four waves of the Canadian Community Health Survey (2003, 2005, 2008, 2012, N = 6393) were utilized to assess lifetime screening and screening in the previous 3 years among women residing in Montreal. Chi-squared statistics were calculated, Poisson regression was utilized to model prevalence ratios, and prevalence differences were calculated. In total, 13.6% of women had never been screened and 12.1% had not been screened in the previous 3 years. Immigrant status was the strongest predictor of never being screened [recent vs non-immigrant: Prevalence Ratio (PR), 3.9 (95% Confidence Interval (CI): 2.9-5.4)] and not having a primary care physician (PCP) was the strongest predictors of not being screened recently [PR = 3.0 (95% CI: 2.3-3.9)]. The two most common reasons for not being screened were not "know[ing] it was necessary" and not "get[ting] around to it." These results provide a description of sub-populations which might benefit from cervical screening interventions: immigrants and women without a PCP. Interventions targeting access to PCPs, expanding training of non-physicians to conduct screening, organized screening, or autoadministered screening test may mitigate inequalities. Future work should assess their acceptability and feasibility, and evaluate the impact of these types of primary care and policy interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".