Cervical Cancer Screening Practice and Associated Factors Among Healthcare Providers in Ethiopia: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Introduction Cervical cancer is a major public health issue worldwide and is the second most common disease in developing nations like Ethiopia. Early screening and treatment are critical for treating cervical cancer. However, there is no systematic review evidence on cervical cancer screening practices. Hence, the goal of this review was to assess cervical cancer screening practices and the factors influencing them among healthcare providers in Ethiopia. Search Strategy and Data Sources This review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 6,818 articles were collected from various international databases, including PubMed, Scopus, Web of Science, ScienceDirect, African Journals Online, Google Scholar, and the Wiley Online Library, covering the period from December 1, 2024 to February 6, 2025. The Newcastle–Ottawa scale (NOS) was employed to evaluate the methodological quality of the studies. Data extraction were carried out using Excel, and the analysis was performed with STATA 11 software. The pooled effect size for cervical cancer screening practices was estimated using a random effects model. Cochran’s Q test and the I 2 statistic were applied to evaluate the heterogeneity among the studies, while the symmetry of the funnel plot and Egger’s test were utilized to detect publication bias. Results In total, 7,781 study participants were involved in 16 studies for this evaluation. The pooled cervical cancer screening practice among healthcare providers was 18.35% (95% CI: 10.98, 25.73). Good knowledge (AOR = 5.09; 95% CI: 2.26, 11.47), having ≥3 years of work experience (AOR = 2.04; 95% CI: 1.10, 3.78), and receiving cervical cancer training (AOR = 3.95; 95% CI: 2.10, 7.33) were significant factors for cervical cancer screening practices. Conclusions More than two-thirds of healthcare providers did not implement cervical cancer screening practices aimed at prevention. To reduce the morbidity and mortality associated with cervical cancer in women, the Ethiopian Ministry of Health and its partners should increase the knowledge of healthcare providers, particularly new employees. This can be achieved by offering training on effective screening practices to prevent complications related to cervical cancer. The review has been recorded under International Prospective Register of Systematic Reviews (PROSPERO) with the number CRD42024565570.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.030 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".