Identifying factors influencing utilization of cervical cancer screening among immigrant women in Canada
Bibliographic record
Abstract
Cervical cancer screening using the Papanicolaou (Pap) smear test has had a significant effect on early detection of invasive cervical cancer. However, the risk of cervical cancer remains high among immigrant women in Canada. The objective of this study was to explore factors influencing the utilization of cervical cancer screening among immigrant women 15-69 years of age in Canada. Data from the 1996-1997 National Population Health Survey were used to conduct a secondary analysis, with logistic regression analysis, of factors associated with use of Pap testing. The findings indicated that 27% of immigrant women in Canada have either never had Pap testing or have had Pap testing completed more than three years ago. With regression analysis, factors significantly related to ever having had Pap testing among immigrant women between 25-69 years of age included: residing 10 years or more in Canada, having a regular doctor, speaking English or French, completion of some post-secondary education, aged 25-39 years, being widowed/divorced/separated, and having been born in US/Europe/Australia. Several interesting interaction effects were noted between income and education, between length of residence in Canada and having a regular doctor, and between education and language use. Being younger and having a regular physician were significantly related to having Pap testing within the past three years. Of these women, the most frequently reported reason for the use of Pap testing was that it was part of a regular check-up. Only a few women experienced problems obtaining Pap testing. Among women who never had a Pap test or had no Pap testing within the last three years, the most common reason for non-use of Pap testing was thinking that it was not necessary. The findings provide direction for identifying immigrant women at risk for not having regular Pap tests. Nurses can help to increase the uptake of Pap testing by providing information about the importance of this relatively simple, life-saving test to immigrant women and by promoting routine physical checkups with a family doctor. Offering women alternative and convenient ways to obtain Pap testing in settings that employ female health-care providers and provide language services may be among the best strategies for improving uptake and regular participation in cervical cancer screening for Canadian immigrant women.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".