Community Report: Understanding How the Lives and Experiences of South Asian Women Impact Participation in Cervical Screening
Bibliographic record
Abstract
This is a community report that summarize the main activities and outcomes of a Concept Mapping study that engaged South Asian service users and service providers in Ontario, to understand how the lives and experiences of South Asian women in Ontario impact their decisions to get screened for cervical cancer. Using this method of Concept Mapping, we identified and prioritized aspects in people's lives that could impact participation in cervical screening. Concept Mapping is a semi-qualitative method that moves beyond identifying themes, to also include participants in the interpretation of interrelationships amongst the themes, and discussion of the findings. Participants brainstormed a total of 210 statements and after idea synthesis, 45 unique and distinct statements were identified. Through sorting and map interpretation, participants identified and labelled six 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. In the end we identified multiple points of intervention that go beyond the individual, to include community and policy. To address underscreening we need to design multi-level interventions that address the identified ideas and the interrelationships among them. This report summarizes the step-by-step series of Concept Mapping activities, the results that came out of it, and key recommendations for improving rates of cervical screening in Ontario.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.002 |
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".