Addressing Underscreening for Cervical Cancer among South Asian Women: Using Concept Mapping to Compare Service Provider and Service User Perspectives of Cervical Screening in Ontario, Canada
Bibliographic record
Abstract
Cervical cancer is largely preventable through screening and treatment of cervical lesions. In the province of Ontario, South Asian women have some of the lowest rates of screening. The roles of service providers-those in healthcare and community services-and their interactions with screen-eligible people can greatly impact the uptake of screening. In our study, we used concept mapping (CM) to engage over 70 South Asian service users (i.e., those eligible for cervical screening) and service providers to identify a range of ideas and experiences that impact uptake of cervical screening for South Asian women, which were then rated by 45 participants in terms of 'importance' and 'ease to address' to encourage participation in cervical screening. Overall, ideas related to knowledge and education were rated as most important and easiest to address by both groups. Some differences were seen with South Asian service users valuing the importance of addressing 'cultural beliefs and influences specific to sexual health' more than service providers, while service providers valued the importance of addressing 'lack of comfort and supportive relationships' more than South Asian service users. Future interventions should target the knowledge and education needs of service users and increase service providers' awareness of cultural beliefs and influences specific to sexual health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".