Important and Feasible Actions to Address Cervical Screening Participation amongst South Asian Women in Ontario: A Concept Mapping Study with Service Users and Service Providers
Bibliographic record
Abstract
Regular cervical screening can largely prevent the development of cervical cancer and innovative methods are needed to better engage people in screening. In Ontario, Canada, South Asian women have some of the lowest rates of screening in the province. In this study, we used concept mapping to engage two stakeholder groups-South Asian service users and service providers-to identify and prioritize points of intervention to encourage the uptake of cervical screening. After participants brainstormed a master list of statements, 45 participants rated the statements based off 'importance' and 'ease to address' in relation to encouraging cervical screening. A bivariate plot (X-Y graph) that shows the average rating values for each statement across the two rating variables (a 'go-zone' display) was produced to display priorities for implementation. Statements that were considered high priority to address reflected issues around education and awareness including understanding and communication related to cervical screening and preventative care, as well as the need for trusted sources of information. Statements that were considered high priority but challenging to implement were centered around fear, stigma, discomfort, family and personal priorities. This study highlighted that stigma, norms and social relations that impact the uptake of screening must be addressed in order for education and awareness raising to be effective and to move people from conviction around screening to action.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".