Knowledge, utilization and barriers of cervical cancer screening among women attending selected district hospitals in Kigali - Rwanda
Bibliographic record
Abstract
BACKGROUND: Cervical cancer is the third most common cancer attacking women globally, and the second in Eastern Africa where Rwanda is located. Regular screening is an effective prevention approach for cervical cancer. Despite that, the screening rate for cervical cancer in Africa is estimated between 10% and 70%, with a number of barriers. This is especially the case in sub-Saharan Africa. In Rwanda, there is limited literature on the rate of use of screening services or the barriers to cervical screening. OBJECTIVE: To assess knowledge, utilization, and barriers of cervical cancer screening among women attending selected district hospitals in Kigali, Rwanda. METHODS: A descriptive cross-sectional study with a structured questionnaire was used to collect data. Nominal 'yes' or 'no' questions were used to gather data on knowledge and utilisation of cervical cancer and its screening. Likert-type scale questions were used to identify different barriers to screening services. Data were analysed using descriptive and inferential statistics. Respondents were selected by systematic random sampling from the database of women attending gynaecology services at three district hospitals in Kigali, Rwanda. RESULTS: Three hundred and twenty-nine women responded to the survey. Half of the respondents (n = 165) had high knowledge level scores on cervical cancer screening. The cervical cancer screening rate was 28.3%. Utilization of screening was associated with knowledge (P = 0.000, r = -0.392) and selected demographic factors (P = 0.000). Individual barriers included poor knowledge on availability of screening services, community barriers included living in a rural area, and health provider and systems barriers included lack of awareness campaigns, negative attitudes of healthcare providers toward clients, and long waiting times; all barriers limit the access to screening services. CONCLUSION: A low rate of cervical cancer screening was identified for women attending selected district hospitals in Kigali-Rwanda due to various barriers. On-going education on cervical cancer and its screening is highly recommended. It is important that trained health providers encourage their clients to have cervical cancer screening and work to reduce related barriers.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".