Cervical cancer screening utilization among healthcare professionals in Ethiopia: systematic review and meta-analysis
Bibliographic record
Abstract
Background: Cancer of the cervix is the second most common cancer among women worldwide, with about over 660 000 new cases and approximately ninety-four percent of the 350 000 cervical cancer-related death happened in low- and middle-income countries. Effective screening initiatives are particularly crucial in preventing cervical cancer in women. Therefore, the purpose of this systematic literature review was to investigate the pooled prevalence of Ethiopian female healthcare professionals' cervical cancer screening utilization. Methods: Published articles were searched from different major international databases (PubMed, Cochrane Library, Scopus, Web of Science, Since Direct, Google Scholar). Direct Google searches were used for additional sources mainly for gray and preprint studies. This review included studies that reported either the use of cervical cancer screening or cervical cancer screening predictors in Ethiopia. All published and unpublished studies through May/2024 and reported in the English language were retrieved to assess eligibility for inclusion in this review. The Newcastle-Ottawa Scale quality assessment tool was used to assess the quality of the included studies and Egger's test was used to assess the publication bias. Results: In order to calculate the pooled prevalence of cervical cancer screening, 2,919 female healthcare professionals participated in the review. Articles were published from 2015 to 2024. The pooled Utilization of cervical cancer screening in Ethiopia, as determined by a meta-analysis of ten articles was 13.59% (95% CI: 7.53, 19.65). Conclusion and recommendation: The estimated/pooled cervical cancer screening utilization was found to be lower than the World Health Organization recommendations as the estimator revealed in the meta-analysis. The low utilization of Cervical Cancer (CCa)screening practice despite they are healthcare professionals is a significant concern that can impact the broader efforts to combat cervical cancer. Based on the this reviews the authors recommend regular monitoring and evaluation of the CCa screening habits of healthcare professionals and the effectiveness of implemented interventions. It is necessary to explore the factors that enable or hinder CCa screening and address the issue through qualitative or mixed-method studies.
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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 | 0.016 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.032 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".