Beyond the numbers: Cervical Cancer Screening Facilitators and Barriers Among Racialized Communities in Peel Region, Ontario
Bibliographic record
Abstract
Context: Cancer is a leading cause of death in Canada. In the province of Ontario, a well-established cervical screening program is in place within a universal health care system. Despite this program, and the benefits of early detection on patient health outcomes, screening disparities persist with racialized populations remaining under screened. Objective and Population Studied: To understand the barriers and facilitators to cervical cancer screening in Peel Region, Ontario—a racially diverse and under screened area. Specifically, to gain insight into: a) screening rate variations amongst racialized populations; b) perceived screening facilitators and barriers; and c) service gaps and potential solutions. Study Design and Analysis: A convergent parallel mixed-methods participatory study was conducted. Population-based health administrative databases that capture screening visits linked with immigration and census data were used to estimate screening rates overall and by sub-population. Concurrently, community research assistants were hired to support participant and stakeholder recruitment. They conducted one-on-one semi-structured interviews which underwent thematic analysis. Dataset: Data from over 20,000 Peel Region residents were analyzed and 42 participants (35 healthcare users and 7 providers) were interviewed. Outcome Measures: To understand factors that foster or hinder cervical cancer screening; to explore variables associated with cervical cancer screening and predictors of screening practices. Results: Participants from the Caribbean (e.g., Jamaica, Trinidad) and Asia (e.g., China, Vietnam) were found to have higher screening rates than individuals from India, Pakistan, and Poland. Individuals not registered to a family physician, and living in lower socioeconomic status areas were less likely to undergo screening. Interviews revealed cultural barriers (e.g., familial obligations as a newcomer), screening stigmas (e.g., fear of social judgement) and the need for culturally responsive cervical cancer screening information. Conclusions: Findings underscore the need for decision makers to avoid a one size fits all screening approach and to shift towards designing and implementing approaches that reflect diverse patient experiences. Additional research is needed to learn from jurisdictions with high screening rates and lower disparities in screening uptake to increase the evidence needed to improve cancer screening amongst racialized populations.
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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 | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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 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".