Perceptions of cervical screening uptake amongst South Asian women in Ontario, Canada: a concept mapping study
Bibliographic record
Abstract
BACKGROUND: Regular cervical screening can significantly reduce the onset and prevalence of cervical cancer. In Ontario, Canada, South Asian women have the lowest rates of cervical cancer screening among major ethnic groups in the province. METHODS: Using an innovative and participant-driven method called Concept Mapping (CM), we set out to understand how the lives and experiences of South Asian women living in Ontario shape their decisions around getting screened for cervical cancer. We engaged over 70 South Asian women and people who serve them in healthcare and community, to drive the CM process. RESULTS: Participants brainstormed 45 unique and distinct statements. Through sorting and map interpretation, participants identified and interpreted 6 clusters amongst the statements: (1) Personal beliefs and misconceptions around cervical cancer; (2) Education and knowledge issues around cervical cancer; (3) Cultural beliefs and influences specific to sexual health; (4) Barriers to prioritizing uptake of cervical screening; (5) System/ infrastructure gaps or inadequacies; and (6) Lack of comfort and supportive relationships in healthcare. Additional analysis shows us the interrelationships between the ideas. Statements within the clusters about education and knowledge issues around cervical cancer, personal beliefs and misconceptions, as well as cultural beliefs and influences specific to sexual health are viewed as distinct beliefs with clear effects on the uptake of cervical screening. More complex interrelationships are seen with the cluster of statements about barriers to prioritizing uptake of cervical screening. CONCLUSIONS: As Ontario and many other jurisdictions around the world seek to strengthen cervical screening efforts in line with national and international goals to eliminate cervical cancer by 2040, it is critical to address underscreening. This CM study recognizes the value of engaging those most impacted by an issue, to identify and prioritize how and where to intervene to address low rates of cervical screening. To address underscreening we need to design multi-level interventions that address the identified ideas and the interrelationships among them.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".