Evidence map›Paper›PMID 42638768›Full record

ArticleFrontiers in public health2026

Practice and application of blended teaching based on RG-UClass in a preventive medicine course.

Sha Liu, Jiale Zhang, Yuqi Shang, Yufei Jiang, Xueyan Zhang, Zhiyong Hu, Caiyun Chen, Wei Mi

Abstract read
In one paragraph

Article in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Sha Liu *School of Public Health, Shandong Medical and Pharmaceutical University, Yantai, China.
Jiale Zhang *The First School of Clinical Medicine, Shandong Medical and Pharmaceutical University, Yantai, China.
Yuqi Shang *The Second School of Clinical Medicine, Shandong Medical and Pharmaceutical University, Yantai, China.
Yufei JiangSchool of Public Health, Shandong Medical and Pharmaceutical University, Yantai, China.
Xueyan ZhangSchool of Public Health, Shandong Medical and Pharmaceutical University, Yantai, China.
Zhiyong HuSchool of Public Health, Shandong Medical and Pharmaceutical University, Yantai, China.
Caiyun ChenSchool of Public Health, Shandong Medical and Pharmaceutical University, Yantai, China.
Wei MiSchool of Public Health, Shandong Medical and Pharmaceutical University, Yantai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: In the post-pandemic era, online teaching has become a cornerstone of higher education, especially for delivering structured medical knowledge. This study assessed the effectiveness of a blended teaching approach using the RG-UClass (Ruijie-UClass) smart platform in preventive medicine courses, aiming to provide empirical evidence of its impact and potential to improve learning outcomes. Methods: This quasi-experimental study enrolled 107 Grade 2019 clinical medicine students. Two intact classes were selected from all eight classes: Class 1 ( Results: Students in the intervention group consistently outperformed the control group on pre-class, in-class, and post-course tests ( Conclusion: The RG-UClass-based blended model demonstrates reproducibility and adaptable flexibility, fostering student interest, initiative, and improved academic performance.

Indexed as

Preventive MedicineStudents, MedicalTeachingCurriculumEducational MeasurementFemaleHumansMaleSurveys and Questionnairesblended teachingcourse practicepreventive medicineRG-UClassteaching model

Identifiers

PMID42638768
PMCPMC13500768

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.