MétaCan
Menu
Back to cohort
Record W3104347935 · doi:10.1186/s13027-020-00334-3

Uptake of cervical cancer screening service and associated factors among age-eligible women in Ethiopia: systematic review and meta-analysis

2020· article· en· W3104347935 on OpenAlexaboutno aff
Asteray Assmie Ayenew, Biruk Ferede Zewdu, Azezu Asres Nigussie

Bibliographic record

VenueInfectious Agents and Cancer · 2020
Typearticle
Languageen
FieldMedicine
TopicCervical Cancer and HPV Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCervical cancerFunnel plotMeta-analysisOdds ratioPublication biasCochrane LibraryConfidence intervalFamily medicineCritical appraisalGynecologyCancerDemographyInternal medicineAlternative medicinePathology

Abstract

fetched live from OpenAlex

Abstract Background Cervical cancer is the leading cause of cancer deaths among women in developing countries. Since cervical cancer is a preventable disease, screening is an important control and prevention strategy, recommended by the World Health Organization (WHO) for all women aged 30 years and older, and even earlier for some high-risk women. Therefore the aim of this study was to assess the uptake of cervical cancer screening among age-eligible women in Ethiopia. Method Review identification was performed through the search of online databases PubMed, Google Scholar, HINARI, EMBASE, Science Direct, Cochrane library, African Journals, and other gray and online repository accessed studies were searched using different search engines. For critical appraisal of studies, Newcastle-Ottawa Quality Assessment Scale (NOS) was used. The analysis was conducted by using STATA 11 software. To test the heterogeneity of studies, the Cochran Q test and I2 test statistics were used. To detect publication bias of the studies, the funnel plot and Egger’s test were used. The pooled prevalence of cervical cancer screening and the odds ratio (OR) with a 95% confidence interval were presented using forest plots. Result Twenty-four studies with a total of 14,582 age-eligible women were included in this meta-analysis. The pooled national level of cervical cancer screening among age-eligible women in Ethiopia was 13.46% (95%CI:11.06,15.86). Knowledge on cervical cancer and screening (OR = 4.01,95%CI:2.76,5.92), history of multiple sexual partners (OR = 5.01, 95%CI:2.61,9.61), women’s age (OR = 4.58, 95%CI:2.81,7.46), history of sexually transmitted disease (OR = 4.83,95%CI:3.02,7.73), Perceived susceptibility to cervical cancer (OR = 3.59, 95%CI:1.99,6.48), getting advice from health care providers (OR = 4.58, 95%CI:3.26, 6.43), women’s educational level (OR = 6.68,95%CI:4.61,9.68), women’s attitude towards cervical cancer and screening (OR = 3.42, 95%CI:2.88,4.06) were the determinant factors of cervical cancer screening uptake among age-eligible women in Ethiopia. Conclusion The pooled prevalence of cervical cancer screening was remarkably low among age-eligible women in Ethiopia. Thus, to increase the uptake of cervical cancer screening among age-eligible women regularly, it is better to create awareness programs for early detection and treatment of cervical cancer, and educational interventions that teach the step-by-step practice of cervical screening to increase women’s attitude for screening. Additionally, it is better to inform every woman is susceptible to cervical cancer, especially after starting sexual intercourse, and screening remains fundamental in the fight against cervical cancer before becoming invasive. Moreover, counseling and improving the confidence of women by health care providers to undergo screening is recommended.

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 imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.032
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.017
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.032
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0170.035
Bibliometrics0.0060.006
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.097
GPT teacher head0.368
Teacher spread0.271 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations65
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueInfectious Agents and CancerSame topicCervical Cancer and HPV ResearchFrench-language works237,207