Bibliographic record
Abstract
Aisha Kamilah O. Lofters\nCervical Cancer Screening Among Ontario’s Urban Immigrants\nDoctor of Philosophy, 2012\nInstitute of Health Policy, Management and Evaluation\nUniversity of Toronto\nBackground: The majority of cervical cancers can be prevented because of the highly effective screening tool, the Papanicolaou (Pap) test. Relevant guidelines recommend routine screening for nearly all adult women. However, inequities in screening exist in Ontario. This dissertation, consisting of three studies, uses administrative data to advance knowledge on barriers to cervical cancer screening for Ontario’s urban immigrant population. \nMethods: First, we developed and validated a billing code-based algorithm for cervical cancer screening. We then implemented this algorithm to examine screening rates in Ontario among women with various sociodemographic characteristics for 2003-2005. Second, we compared the prevalence of appropriate cervical cancer screening in Ontario in 2006-2008 among immigrant women from all major geographic regions of the world and Canadian-born women. Third, we used a stratified multivariate analysis to determine if the independent effects of various factors that could serve as screening barriers were modified by region of origin for immigrant women for 2006-2008. \nResults: Our first study showed that our algorithm was 99.5% sensitive and 85.7% specific, and that screening inequities in Ontario’s urban areas are largest among women 50 years and older, living in the lowest-income neighbourhoods and new to the province. In our second study, we determined that immigrant women had significantly lower screening rates than their peers, with the most pronounced differences seen for South Asian women aged 50 years and above. In the final study, we demonstrated that living in the lowest-income neighbourhoods, being younger than 35 years or older than 49 years, not being enrolled in a primary care enrolment model, having a male provider, and having a provider from the same region of the world each significantly influenced screening for immigrant women regardless of region of origin.\nConclusion: These results add to the literature on health equity in cancer screening. Our findings demonstrate that Ontario’s urban immigrant women experience significant inequities in cervical cancer screening, and may offer guidance toward targeted patient and physician interventions to decrease screening gaps.
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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 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".