Association between health literacy and mortality: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: To identify the relationship between health literacy (HL) and mortality based on a systematic review and meta-analysis. METHODS: Literature published from database inception until July 2020 was searched using the PubMed and Web of Science databases, using relevant keywords and clear inclusion and exclusion criteria. The search was limited to English language articles. Two reviewers independently selected studies and extracted data. Pooled correlation coefficients and their 95% confidence intervals (CI) between HL and mortality were estimated using Stata 15.0 software. Potential sources of heterogeneity were explored using subgroup analysis, sensitivity analysis, and meta-regression. Quality of the original studies that were included in the meta-analysis was evaluated using the Newcastle-Ottawa Scale. A funnel plot and Egger's test were used to determine whether significant publication bias was present. RESULTS: Overall, 19 articles were included, reporting on a total of 41,149 subjects. Eleven were prospective cohort studies, and all articles were considered "good" quality. The most used screening instruments were the short Test of Functional Health Literacy (S-TOFHLA) in Adults and the Brief Health Literacy Screen (BHLS). Among 39,423 subjects (two articles did not report the number of patients with low HL), approximately 9202 (23%) had inadequate or marginal HL. The correlation coefficient between HL and mortality was 1.25 (95%CI = 0.25-0.44). CONCLUSION: Lower HL was associated with an increased risk of death. This finding should be considered carefully and confirmed by further research.
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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 | 0.021 | 0.049 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.045 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".