Evidence map›Paper›PMID 36860941›Full record

ArticleFrontiers in surgery2023

"LEARN", a novel teaching method for Chinese clinical clerkship: A cross-sectional study.

Xiangyu Chen, Matthew F Gong, Song Wu, Jinshen He

Abstract read
In one paragraph

Article in Frontiers in surgery, 2023. 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

4 authors.

Xiangyu ChenDepartment of Orthopaedic Surgery, Central South University Third Xiangya Hospital, Changsha, China.
Matthew F GongDepartment of Orthopaedic Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA, United States.
Song WuDepartment of Orthopaedic Surgery, Central South University Third Xiangya Hospital, Changsha, China.
Jinshen HeDepartment of Orthopaedic Surgery, Central South University Third Xiangya Hospital, Changsha, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Despite the clerkship being crucial in the training of a future doctor, no widely accepted education model has been proposed. This study devised a new model for clinical clerkship rotations, titled "LEARN" for Lecture, English-video, Advisor, Real-case and Notion, and evaluated whether the LEARN model is appropriate for medical education in China. Methods: A cross-sectional study was performed among 101 fourth-year students from the Xiangya School of Medicine during an Orthopaedic Surgery clerkship rotation in the Third Xiangya Hospital. They were divided into seven groups and took clerkship based on the LEARN model. A questionnaire was collected at the conclusion to measure learning outcomes. Results: The LEARN model was highly accepted with the acceptance of five sessions being 95.92% (94/98), 93.88% (92/98), 96.98% (97/98), 100% (98/98) and 96.94% (95/98). The outcomes of two genders were comparable, whereas a difference was observed in the test score among groups (group 3 scored 93.93 ± 5.20, higher than others). Quantitative analysis showed that positive correlations existed in participation in the Notion (Notion means students' case discussion) section with leadership ( Conclusion: Our results support the LEARN model is a promising method for medical clerkship in China. Further research involving more participants and more meticulous design is planned to test its efficacy. For refinement, educators may try to promote students' participation in the English-video session.

Indexed as

clerkshipclinical skillscross-sectional studymedical education—clinical skills trainingundergraduate (MeSH)

Identifiers

PMID36860941
PMCPMC9968847

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.