HEALTH LITERACY AND ITS IMPACT ON OUTCOMES IN PATIENTS WITH ATRIAL FIBRILLATION: A PROSPECTIVE COHORT STUDY
Bibliographic record
Abstract
Abstract Background Health literacy (HL) is a risk factor for clinical outcomes in patients with cardiovascular diseases. This study explored HL levels and their impact, alongside clinical and nursing factors, on mortality, hospital readmissions, and emergency department (ED) visits in patients with atrial fibrillation. Methods A prospective cohort study was conducted at an Italian university hospital among adults with atrial fibrillation as the primary medical diagnosis between December 2020 and June 2021. Data collection included HL levels and sociodemographic, clinical, and nursing–related variables (e.g., nursing diagnoses identified within the first 24 hours of hospitalization and the number of nursing interventions delivered during the hospital stay). Patients were followed for 12 months to assess the study outcomes. Cox proportional hazard models analyzed associations between HL, nursing and clinical complexities, and outcomes. Results Among 441 patients (mean age 73.6 ± 11.9 years), over 70% had inadequate HL. During follow–up, 138 patients were lost, while 57 (17.8%) died, 117 (38.6%) were readmitted, and 99 (35.9%) had ED visits. The number of nursing diagnoses predicted mortality in univariable (HR 1.23; 95% CI: 1.14–1.32; p‹0.001) and multivariable analysis (HR 1.18; 95% CI: 1.06–1.32; p‹0.005). The HL level independently predicted mortality (HR 1.40; 95% CI: 1.01–1.96; p‹0.05). For readmissions, predictors included age (HR 1.05; 95% CI: 1.03–1.07; p‹0.001), length of stay (HR 0.92; 95% CI: 0.87–0.97; p‹0.005), number of nursing diagnoses (HR 1.30; 95% CI: 1.15–1.46; p‹0.001), and HL levels (HR 1.46; 95% CI: 1.15–1.86; p‹0.005). For ED visits, predictors included age (HR 1.04; 95% CI: 1.02–1.07; p‹0.001), number of nursing diagnoses (HR 1.42; 95% CI: 1.26–1.61; p‹0.001), and HL levels (HR 1.34; 95% CI: 1.01–1.79; p‹0.05). Length of stay and the number of chronic conditions were not found to be significant predictors of ED visits. Nursing interventions were not significant predictors of mortality; however, they were protective regarding hospital readmissions (HR 0.75; 95% CI: 0.67–0.84; p‹0.001) and ED visits (HR 0.61; 95% CI: 0.53–0.69; p‹0.001). Conclusions HL and nursing complexity were predictors across several outcomes, while nursing interventions reduced hospital readmissions and ED visits. These results could be used to promote personalized nursing care covering strategies to address inadequate HL in atrial fibrillation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".