Prevalence of non-adequate health literacy in Europe: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background Many studies show that a low level of health literacy (HL) is associated with several adverse outcomes. The aim of this systematic review was to estimate the prevalence of non-adequate HL (NAHL) in European countries and outline the main needs for interventions. Methods The systematic search was performed in April 2019 and updated in June 2019. PubMed, Embase and Scopus were searched. Articles were considered eligible if they were cross-sectional studies published in English after 2000 and estimating the NAHL prevalence in European countries. Globally, 15490 articles were retrieved. Adapted Newcastle-Ottawa Scale was applied for the quality assessment. Several stratified meta-analyses were carried out. We also performed a meta-regression analysis to test the association between variables and NAHL. Results In total, 59 articles of heterogeneous quality were included, providing data for 98 studies to include in the proportion meta-analysis. Overall, quantitative analysis yielded a pooled NAHL prevalence of 40% (95%CI, 36%-43%). Despite the prevalence varied considerably by country, it seemed to follow a geographic gradient, with the northern countries clearly having a lower prevalence than the other European counterparts. The pooled prevalence estimates (PEs) varied significantly according to the different type of HL assessment method applied. Also, high study quality was found to be significantly associated with a reduction of NAHL in the PEs. Grouping the sample in general population, oncology patients, chronic disease patients and refugees, the meta-regression analysis showed a significantly lower prevalence of NAHL in oncology patients. Conclusions Although the PEs varied in relation to several factors (e.g. either among population groups, or depending on the HL assessment method), this study shows that more than one in every three surveyed participants had NAHL. Targeted strategies and coordinated policies aiming at improving HL in the Region are needed. Key messages Despite several variations, a significant proportion of European population has non-adequate health literacy. Targeted public health strategies of intervention are crucial to address this deficit.
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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 | 0.019 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.035 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".