The association between adherence to cancer screening programs and health literacy: A systematic review and meta-analysis
Bibliographic record
Abstract
The effectiveness of a cancer screening program relies on its adherence rate. Health literacy (HL) has been investigated among the factors that could influence such participation, but the findings are not always consistent. The aim of this meta-analysis was to summarize the evidence between having an adequate level of HL (AHL) and adherence to cancer screening programs. PubMed, Scopus, and Web of Science were searched. Cross-sectional studies, conducted in any country, that provided raw data, unadjusted or adjusted odds ratio (OR) on the associations of interest were included. The quality of the studies was assessed with the Newcastle-Ottawa Scale. Inverse-variance random effects methods were used to produce pooled ORs and their associated confidence interval (CI) stratified by time interval (e.g., undergoing screening in the last period, or at least once during lifetime) for each cancer type, considering unadjusted and adjusted estimates separately. A sensitivity analysis was performed for those studies providing more estimates. Overall, 15 articles of average-to-good quality were pooled. We found a significant association between AHL and higher screening participation for breast, cervical and colorectal cancer, independently of other factors, both overall (N = 7, aOR = 1.73; 95% CI: 1.27-2.36; N = 3, aOR = 1.64; 95% CI: 1.30-2.09; and N = 5, aOR = 1.25, 95% CI: 1.12-1.39, respectively) and in most time-stratified analyses. The sensitivity analyses confirmed these results. Health literacy seems to be critical for an effective cancer prevention. Given the high prevalence of illiterate people across the world, a long-term action plan is needed.
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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 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.047 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".