Knowledge and Utilization of Cervical Cancer Screening Service among Women in Ethiopia: A Systemic Review and Meta-Analysis
Bibliographic record
Abstract
Background: The purpose of this meta-analysis was to assess the association between knowledge and utilization of cervical cancer screening services among women in Ethiopia. Previous tests of the association of knowledge and utilization of cervical cancer screening services have yielded inconsistent results. Using data from 12 studies, from different regions of Ethiopia we performed a meta-analysis with a specific focus on women's utilization of cervical cancer screening service. Methods: Electronic databases were searched from 2014 to 2019.on reference manager software reporting knowledge and utilization of cervical cancer screening service. Data extraction and assessment were guided by PRISMA checklist. Observational studies and studies with Newcastle-Ottawa Scale score of 5 or greater were included in the review. The pooled adjusted Odds ratios (OR)) and 95% confidence intervals were obtained using random effect model We applied the random-effects analytic model and calculated a pooled odds ratio Results: A total of 12 observational studies involving 4704 participants, 1235 of which had utilization of cervical cancer screening service were eligible for inclusion in this meta-analysis The summary OR for utilization of cervical cancer screening services comparing Women who know cervical cancer screening service versus Women who did not know cervical cancer screening service was 1.16 (95%CI 0.28 to 4.77), P=0.813, I2=96.23% %). There was significant= heterogeneity for all studies (Q=291.78; P=0.000; I2=96.23%. No publication bias was observed (Egger’s test: P=0.693, Begg’s test: P=0.131). 47.16% (2218) Women who know cervical cancer screening service 11.41% (537) engaged in the utilization of cervical cancer screening services. The proportion of utilization of cervical cancer screening service among women aged >20 years was 18.22% in 6 of the 12 studies. The overall proportion of utilization of cervical cancer screening was 24.21% and 28.08% among having knowledge of cervical cancer screening service and not having knowledge cervical cancer screening service respectively. Conclusion: Our findings suggest that knowledge of cervical cancer screening service is not directly related to the likelihood that they practice cervical cancer screening. The relationship between age and utilization of cervical cancer screening should be explored further.
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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 | 0.018 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.049 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".