Construction of a health literacy prediction model for diabetic patients: A multicenter study

As one of the few HL prediction models for diabetic patients in mainland China that is built based on multi-center survey data and evaluated through multi-center assessment, this model can quickly identify patients with insufficient HL using a small amount of objective personal information.

Authors

Institutions

Publication Details

Journal
Digital Health
Published
2025-01-01
DOI
https://doi.org/10.1177/20552076241311735
Primary Topic
Health Literacy and Information Accessibility
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Construction of a health literacy prediction model for diabetic patients: A multicenter study

Kui Jiang, Yalan Chen, Zepeng Wang, Fan Jiang
Digital Health
Health Literacy and Information Accessibility
article

Construction of a health literacy prediction model for diabetic patients: A multicenter study

Kui Jiang, Yalan Chen, Zepeng Wang, Fan Jiang
article en

Abstract

As one of the few HL prediction models for diabetic patients in mainland China that is built based on multi-center survey data and evaluated through multi-center assessment, this model can quickly identify patients with insufficient HL using a small amount of objective personal information.

Digital HealthVol. 11
Nantong University (CN)
National Natural Science Foundation of China, Government of Jiangsu Province
Quality Education
Openalex Percentile: Top 99%
Health Literacy and Information Accessibility
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.