Uptake of Cervical Cancer Screening and Associated Factors Among Women Attending Outpatient Services in Rwamagana Hospital, Rwanda
Bibliographic record
Abstract
Background: Cervical cancer is a global public health threat for women. Rwanda Ministry of Health recommends screening as preventive strategy. However, the screening remains low in Rwanda. Objective: To determine the uptake level of cervical cancer screening and associated factors among Rwandan women. Methods: A quantitative analytical cross-sectional study design was used. We recruited 178 participants using convenience sampling from an estimated 320 women who attended outpatient department in the previous month. The sample size was calculated using the Yamane's formula. We used chi-square test, t-test and multiple logistic regression analysis to analyse data. Results: A total of 178 (100%) participants completed the survey. Forty-one (23%) participants had undertaken cervical cancer screening. Knowledge (OR: 1.26,95% CI:1.069-1.485, p=.006) and income were predictors of cervical cancer screening uptake. Participants earning RWF ≥ 63,751 were more likely to uptake cervical cancer screening (OR:11.141, 95% CI:3.136-39.571, p< .001) compared to those earning less than RWF 25,500 monthly. Conclusion: Cervical cancer screening uptake among study population was low. Participants with more knowledge and high-income were more likely to uptake cervical cancer screening. Improving women's knowledge and socioeconomic situation would improve the uptake of cervical cancer screening.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".