Effect of health literacy on hospital readmission among patients with heart failure: A protocol for systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Patients with heart failure have a high rate of health literacy deficiency, and their hospital readmission is a great burden. Whether health literacy affects hospital readmission remains controversial. OBJECTIVE: To investigate the impact of health literacy on hospital readmission among heart failure patients. METHOD: Relevant keywords were used to search for Chinese and English literature from Web of Science, PubMed, Cochrane Library, China National Knowledge Infrastructure, VIP Database for Chinese Technical Periodicals, Digital Journal of Wanfang Data, and Chinese BioMedical Literature Database. Newcastle-Ottawa Scale was used to assess the quality of the studies. Statistical analysis was performed using Stata 15.0, the fixed effect model was used to calculate the pooled effect estimate, and Begg's and Egger's tests were applied to assess the presence of publication bias. RESULTS: Nine studies, involving 4093 heart failure patients, were included in this study. The overall rate of inadequate health literacy was 40.3%. Among these articles, 6 were included in the meta-analysis to calculate the pooled effect. The results indicated that, when compared with patients with adequate health literacy, those with inadequate health literacy had a relative risk of hospital readmission of 1.01, which increased to 1.14 after adjusting for follow-up time, the result was not significant (P = .09). CONCLUSIONS: About 2 out of 5 heart failure patients had inadequate health literacy, and there was no statistical association between health literacy and hospital readmission among these patients. This finding should be carefully considered and confirmed in further studies.
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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 | 0.052 | 0.078 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.020 | 0.038 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.030 | 0.002 |
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".