Understanding How the Lives and Experiences of South Asian Women Living in Ontario Shape Their Decisions Around Getting Screened for Cervical Cancer: A Concept Mapping Study
Bibliographic record
Abstract
With timely screening, cervical cancer is largely preventable. However, certain subgroups of people with a cervix are disproportionately underscreened. In Ontario, Canada, South Asian women have some of the lowest rates of screening. Innovative approaches are needed to identify ways to address underscreening. In my dissertation, I sought to answer the research question: how do the lives and experiences of South Asian women living in Ontario shape their decisions around getting screened for cervical cancer? I used Concept Mapping to address three main objectives: 1) to identify the experiences in the lives of South Asian women living in Ontario that shape decisions to get screened for cervical cancer (paper 1); 2) to uncover similarities and differences amongst South Asian women (service users) and service providers in their perceptions of the importance and ease to address identified barriers to encourage cervical screening (paper 2); and 3) to identify and prioritize action items to encourage cervical screening amongst South Asian women to increase current screening rates. From September 2022 to August 2023, I recruited more than 70 participants to participate in: brainstorming, sorting, rating and map interpretation. Through sorting, six thematic clusters were identified amongst the 45 statements that came out of brainstorming: 1) Personal beliefs and misconceptions around cervical screening; 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. Through sub-group analysis, I uncovered differences such as South Asian service users valuing the importance of addressing ‘cultural beliefs and influences specific to sexual health’ more than service providers. Lastly, analysis of the rating data identified specific high priority areas of intervention, including education around cervical cancer, cervical screening, and preventative care, as well as having trusted sources of information. Additional areas identified as high importance but challenging to address were fear, stigma, norms and social relations. The findings demonstrate that multiple interventions that cross-cut multiple levels are needed, as culture, society, healthcare, and other larger structures influence individual actions.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".