Cervical Cancer Screening Among Immigrant Women in Canada: Framing the Barriers through Solution Oriented Lens
Bibliographic record
Abstract
We have summarized the research regarding barriers to cervical cancer screening among immigrant women in Canada. We conducted a comprehensive search of published and grey literature to capture barriers through the perspectives of immigrant populations, healthcare providers, and stakeholders. Our initial search yielded 687 articles and, after applying the inclusion criteria, we identified 28 studies for final synthesis. We used a thematic analysis approach to categorize the barriers identified across the studies. Six major thematic categories emerged: (a) economic barriers; (b) healthcare system-related barriers; (c) cultural barriers; (d) language barriers; (e) knowledge-related barriers; and (f) individual-level barriers. Within these thematic categories, patients’, healthcare providers’, and stakeholders’ perspectives were presented to provide an outline on which future engagement toward solutions could be planned. Using a thematic analysis of the barriers helps organize the material, but grounding the themes and the identified barriers within a theoretical framework is helpful when considering possible solutions. Anchoring our identified barriers within the theoretical framework of Social Ecological Model (SEM) offers a holistic overview of the multilevel barriers faced by immigrant women in accessing cervical cancer screening and helps explain these findings in a solution-oriented way. This grounding of barriers within the SEM framework will aid in developing interventions directed at mitigating barriers.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".