Knowledge, Barriers, and Motivators to Cervical Cancer Screening in Rwanda: a Qualitative Study
Bibliographic record
Abstract
Abstract Background: Cervical cancer is a global public health problem with marked geographical disparity. There are high morbidity and mortality rates in low- and middle-income countries (LMICs) associated with low screening rates. In 2020, in Rwanda, 3.7 million women aged 15–59 years were at risk of developing cervical cancer. It is the most commonly diagnosed female cancer in Rwanda with an incidence rate of 42 cases per 100,000 women per year. Despite Rwanda being the first African country to vaccinate against human papillomavirus (HPV) with a three-dose regimen, vaccination coverage of nearly 93 % in the target population of girls aged < 15 years, and having an established cervical cancer screening program, recent studies have found low screening rates. Our study sought to determine knowledge, motivators, and barriers to cervical cancer screening.Methods: This was a qualitative phenomenological study conducted in an urban health facility (Muhima district hospital) and a rural health center (Nyagasambu health center) offering cervical screening services in Rwanda. Focus group discussion (FGD) participants were women seeking these services and other women attending the health facility for any reason, as well as female staff working in these health centers. We also interviewed a key informant (KI) from the national organization. FGD and KI interviews were recorded and transcribed, and the data were analyzed using thematic analysis.Results: Thirty women were enrolled in the FGDs, with an average age of 39 years. Many FGD women showed knowledge about cervical cancer existence and prevention methods. However, fear for pain, lack of knowledge about screening, how and where the screening was done, and privacy concerns were recurring subthemes. Key-informant and some FGDs participants have also mentioned a lack of health insurance as a possible barrier for cervical cancer screening.Conclusions: Barriers to the utilization of cervical cancer screening program in Rwanda are related to poor information about cervical cancer and the importance of screening among women, as well non-adherence to medical insurance schemes. Population sensitization through campaign and community outreach activities could have a positive impact on increasing the uptake of cervical cancer screening in Rwanda.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".