What is the Prevalence of Low Health Literacy in European Union Member States? A Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND: Many studies have shown that low health literacy (HL) is associated with several adverse outcomes. In this study, we systematically reviewed the prevalence of low HL in Europe. METHODS: PubMed, Embase, and Scopus were searched. Cross-sectional studies conducted in the European Union (EU), published from 2000, investigating the prevalence of low HL in adults using a reliable tool, were included. Quality was assessed with the Newcastle-Ottawa Scale. Inverse-variance random effects methods were used to produce pooled prevalence estimates. A meta-regression analysis was performed to assess the association between low HL and the characteristics of the studies. RESULTS: The pooled prevalence of low HL ranged from of 27% (95% CI: 18-38%) to 48% (95% CI: 41-55%), depending on the literacy assessment method applied. Southern, Western, and Eastern EU countries had lower HL compared to northern Europe (β: 0.87, 95% CI: 0.40-1.35; β: 0.59, 95% CI: 0.25-0.93; and β: 0.72, 95% CI: 0.06-1.37, respectively). The assessment method significantly influenced the pooled estimate: compared to word recognition items, using self-reported comprehensions items (β: 0.61, 95% CI: 0.15-1.08), reading or numeracy comprehensions items (β: 0.77, 95% CI: 0.24-1.31), or a mixed method (β: 0.66, 95% CI: 0.01-1.33) found higher rates of low HL. Refugees had the lowest HL (β: 1.59, 95% CI: 0.26-2.92). Finally, lower quality studies reported higher rates of low HL (β: 0.56, 95% CI: 0.06-1.07). DISCUSSION: We found that low HL is a public health challenge throughout Europe, where one in every three to almost one in every two Europeans may not be able to understand essential health-related material. Additional research is needed to investigate the underlying causes and to develop remedies. PROSPERO REGISTRATION: CRD42019133377.
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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 | 0.024 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.032 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".