Predictors of non-adherence to cervical cancer screening among immigrant women in Ontario, Canada
Bibliographic record
Abstract
Cervical cancer is one of the most common types of cancer among women and is largely preventable with regular screening using Papanicolau (Pap) tests. In Canada, all provinces have regular screening programs, although with slightly differing recommendations. Previous research has found that immigrant women, who are a large proportion of the Canadian population, are at higher risk of being under-screened, or non-adherent to the recommended screening frequency. Using data from the 2017 Canadian Community Health Survey, this study examined: (1) the extent to which immigration status and time since immigration are associated with Pap test adherence in Ontario, and (2) predictors of Pap test adherence for immigrants and Canadian born populations in Ontario, Canada's most populous province, with a focus on the role of racial or ethnic identity among immigrants. Estimates of 3-year test adherence were 71.3 % (95 %CI: 66.9-75.7) among immigrant women and 75.4 % (95 %CI: 73.1-77.1) among non-immigrant women. Recent immigrants (6-10 years in Canada) had lower adherence (63.5 %, 95 %CI: 48.0-80.0). Logistic regression models found that immigrant women had lower adherence than Canadian-born women, controlling for age, household income, education, and having a primary care physician. Subgroup analysis found that South Asian immigrant women were least likely to be adherent. These results support targeted programming to increase screening adherence among recent immigrants and raise concerns regarding potential barriers to screening. Data that allow better disaggregation of racial and ethnic identities are important for better understanding the potential implications of these patterns for racial inequities in health.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".