Factors Associated with Underscreening for Cervical Cancer among Women in Canada
Bibliographic record
Abstract
BACKGROUND: Cervical cancer is the second most common cancer among women worldwide. Failure to prevent cervical cancer is partly due to non-participation in regular screening. It is important to plan and develop screening programs directed towards underscreened women. In order to identify the factors associated with underscreening for cervical cancer among women, this study examined Pap test participation and factors associated with not having a time-appropriate (within 3 years) Pap test among a representative sample of women in Ontario, Canada using Canadian Community Health Survey (CCHS) data. MATERIALS AND METHODS: Univariate analyses, cross-tabulations, and logistic regression modeling were conducted using cross-sectional data from the 2007-2008 CCHS. Analyses were restricted to 13,549 sexually active women aged 18-69 years old living in Ontario, with no history of hysterectomy. RESULTS: Almost 17% of women reported they had not had a time-appropriate Pap test. Not having a time-appropriate Pap test was associated with being 40-69 years old, single, having low education and income, not having a regular doctor, being of Asian (Chinese, South Asian, other Asian) cultural background, less than excellent health, and being a recent immigrant. CONCLUSIONS: Results indicate that disparities still exist in terms of who is participating in cervical cancer screening. It is crucial to develop and implement cervical cancer screening programs that not only target the general population, but also those who are less likely to obtain a Pap tests.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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".