Determinants of cervical cancer screening intention among reproductive age women in Ethiopia: A systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Cervical cancer is a leading cause of cancer-related mortality in Ethiopia, despite being preventable. Screening programs remain underutilized despite multiple initiatives. This systematic review and meta-analysis aimed to assess the pooled prevalence of intention to undergo cervical cancer screening and its associated factors among Ethiopian women, addressing a significant gap in national data. METHODS AND MATERIALS: This systematic review and meta-analysis followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Databases such as PubMed, EMBASE, CINAHL, Web of Science, Cochrane Library, HINARI, Google Scholar, and African Journals online were searched using specific keywords and Medical Subject Headings (MeSH). Studies were assessed using a standardized appraisal format adapted from the Newcastle-Ottawa Scale (NOS). Data extraction and analysis were performed using Microsoft Excel-10 and STATA 17 software, respectively. Heterogeneity was evaluated with the I2 statistic and publication bias was examined using Egger's test. Meta-analysis employed a random-effects model. RESULT: Out of the 750 articles retrieved, nine were included in this systematic review and meta-analysis. The pooled prevalence of intention to screen for cervical cancer in Ethiopia was 33% (95% CI: 9%-56%). Factors significantly associated with intention to undergo cervical cancer screening included favorable attitude (POR = 2.15, 95% CI: 1.29, 4.26), good knowledge about cervical cancer screening (POR: 3.49; 95% CI: 2.04, 6.93), and direct subjective norm (POR: 1.54; 95% CI: 1.32, 3.54). CONCLUSION: Based on the findings of this meta-analysis, it was observed that women's intention toward cervical cancer screening was low. Determinants identified included favorable attitude, direct subjective norm, and good knowledge of cervical cancer screening. To enhance women's intention for cervical cancer screening, strategies, and activities should be developed to positively influence perceptions among women and those who influence their decisions. Additionally, efforts to enhance public awareness about cervical cancer and its prevention are crucial.
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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 | 0.017 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.045 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".