Relationship Between Stroke Knowledge, Health Information Literacy, and Health Self- Management Among Patients with Stroke: Multicenter Cross-Sectional Study
Bibliographic record
Abstract
Background: The World Health Organization highlights the essential role of effective self-management in the prevention and control of chronic diseases, noting that proper self-management can significantly slow disease progression. Stroke, which ranks as the third leading cause of death worldwide, is often accompanied by inadequate self-management among patients. While health information literacy (HIL) has been shown to influence self-management in conditions such as diabetes and hypertension, its role as a mediating factor linking disease perception and health behavior in patients with stroke remains insufficiently explored. Addressing this research gap is vital for developing targeted interventions. Objective: The aim of this study was to investigate the current status of HIL, stroke knowledge, and self-management abilities among patients with stroke in Southwest China. Additionally, the study analyzed the relationships among these three factors and their mechanisms of action, providing evidence to inform the development of effective health education strategies for enhancing self-management in patients with stroke. Methods: A multicenter cross-sectional design was employed, enrolling 514 patients with stroke from the neurology departments of three tertiary general hospitals in Chengdu between September 2022 and March 2023. Data collection used a standardized set of scales: the health information literacy questionnaire for stroke assessed HIL, the stroke prevention questionnaire evaluated knowledge levels, and the stroke self-management assessment scale measured self-management abilities. Regression analysis and bootstrap mediation effect testing were conducted using SPSS software (version 26.0). Results: Patients with stroke had a mean (SD) score of 17.61 (6.46) for stroke knowledge, 61.17 (13.58) for HIL, and 158.70 (19.07) for self-management skills. Correlation analysis indicated a positive correlation of stroke knowledge with both self-management (r=0.668; P<.001) and HIL (r=0.138; P<.001). The mediation test showed a significant mediating effect of HIL between stroke knowledge and self-management (β=0.543; 95% CI: 0.431-0.663), with an effect share of 82.77%. Conclusions: There is a correlation between HIL and self-management; the higher the HIL, the better is the self-management behavior. Furthermore, HIL partially mediates the effect of stroke knowledge on self-management.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".