Cervical cancer screening uptake in Sub-Saharan Africa: a systematic review and meta-analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The objective of this study is to estimate the pooled uptake of cervical cancer screening and identify its predictors in Sub-Saharan Africa. Systematic review and meta-analysis. We searched PubMed, EMBASE, CINAHL, African Journals OnLine, Web of Science and Scopus electronic databases from January 2000 to 2019. All observational studies published in the English language that reported cervical cancer uptake and/or predictors in Sub-Saharan Africa were initially screened. We assessed methodological quality using the Newcastle-Ottawa Scale. An inverse variance-weighted random-effects model meta-analysis was performed to estimate the pooled uptake and odds ratio (OR) of predictors with a 95% confidence interval (CI). The I2 test statistic was used to check between-study heterogeneity, and the Egger's regression statistical test was used to check publication bias. We initially screened 3537 citations and subsequently 29 studies were selected for this review, which included a total of 36,374 women. The uptake of cervical cancer screening in Sub-Saharan Africa was 12.87% (95% CI: 10.20, 15.54; I2 = 98.5%). A meta-analysis of seven studies showed that knowledge about cervical cancer increased screening uptake by nearly five times (OR: 4.81; 95% CI: 3.06, 7.54). Other predictors of cervical screening uptake include educational level, age, Human Immune deficiency Virus (HIV) status, contraceptive use, perceived susceptibility and awareness about screening locations. Cervical screening uptake is low in Sub-Saharan Africa as a result of several factors. Health outreach and promotion programmes to target these identified predictors are required.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.019 | 0.003 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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 it