3.4-O8Access and utilization of cervical cancer screening services among four African immigrant communities in Finland: a qualitative study
Bibliographic record
Abstract
Background: Cervical cancer is a leading cause of death among women in the middle-low-income countries due to lack of screening accessibility. It is imperative to understand how immigrant women use cervical cancer preventive services in countries with free screening programs. Objectives: This study aimed to explore cervical cancer screening awareness, facilitating factors and barriers to use of cervical cancer screening services among four African immigrant communities in Finland. Methods: In this exploratory qualitative study, we conducted 9 focus groups with 30 participants. The immigrant women’s age ranged 27-40. They had migrated from Cameroon, Ghana, Kenya, and Nigeria, and had been living in Finland from 1-5 years. The focus group discussions were tape-recorded, transcribed verbatim and analysed for identification of central themes for improving screening participation. Results: Participants had different levels of awareness about screening and cancer prevention, varying from considerable knowledge to no information and no participation in screening. Facilitating factors included: screening is free, screening is for early cancer detection, and screening could be accessed through ante-natal and post-natal care clinics. Barriers to screening were: limited language skills, lack of screening awareness and lack of follow-up information after screening. Conclusions: These preliminary result demonstrate that to improve cervical screening participation among these immigrant groups, adequate screening information and culturally-designed programmes are needed. These include writing screening invitation letters, test results and educational information in English; creating screening awareness through immigrants’ organizations or centres and social media. More attention should be paid to those women who are not using reproductive health services. Main messages Creating screening awareness with adequate information through many channels could increase screening participation rate among the immigrants. Developing effective policies and programmes requires a health equity perspective to tailor initiatives to the particularities of immigrant groups.
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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 | 0.004 | 0.003 |
| 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 | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".