Evidence map›Paper›PMID 39731112›Full record

Trial reportBMC medical education2024

Implementation and evaluation of an optimized surgical clerkship teaching model utilizing ChatGPT.

Yi Huang, Bei-Bei Xu, Xiu-Yan Wang, Yun-Cheng Luo, Miao-Miao Teng, Xuejian Weng

Abstract readRandomized Controlled Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in BMC medical education, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 3 pooled it
–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

7 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Review
  5. Article
  6. Review
  7. Review
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

6 authors.

Yi HuangDepartment of General Surgery, Wenzhou Third Clinical Institute Affiliated to Wenzhou Medical University, The Third Affiliated Hospital of Shanghai University, Wenzhou People's Hospital, Wenzhou Maternal and Child Health Care Hospital, Wenzhou, Zhejiang, 325000, China.
Bei-Bei XuDepartment of Gastroenterology, Wenzhou Third Clinical Institute Affiliated to Wenzhou Medical University, The Third Affiliated Hospital of Shanghai University, Wenzhou People's Hospital, Wenzhou Maternal and Child Health Care Hospital, No. 57 Cang Hou Street, Wenzhou, Zhejiang, 325000, China.
Xiu-Yan WangDepartment of Gastroenterology, Wenzhou Third Clinical Institute Affiliated to Wenzhou Medical University, The Third Affiliated Hospital of Shanghai University, Wenzhou People's Hospital, Wenzhou Maternal and Child Health Care Hospital, No. 57 Cang Hou Street, Wenzhou, Zhejiang, 325000, China.
Yun-Cheng LuoDepartment of General Surgery, Wenzhou Third Clinical Institute Affiliated to Wenzhou Medical University, The Third Affiliated Hospital of Shanghai University, Wenzhou People's Hospital, Wenzhou Maternal and Child Health Care Hospital, Wenzhou, Zhejiang, 325000, China.
Miao-Miao TengDepartment of Gastroenterology, Postgraduate Training Base Alliance of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China.
Xuejian WengDepartment of Gastroenterology, Wenzhou Third Clinical Institute Affiliated to Wenzhou Medical University, The Third Affiliated Hospital of Shanghai University, Wenzhou People's Hospital, Wenzhou Maternal and Child Health Care Hospital, No. 57 Cang Hou Street, Wenzhou, Zhejiang, 325000, China. wengxuejian001@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThis study aims to explore the effect of an innovative teaching model incorporating ChatGPT on medical students' learning outcomes, compliance with learning activities, and overall satisfaction with the learning process.

methodsA cohort of 64 students participating in general surgery clerkships at Wenzhou People's Hospital during the 2022-2023 academic year were randomly assigned into 4 groups, each comprising 16 students. Two of these groups were designated as the study group, where ChatGPT was employed as a supplementary educational tool. The remaining 2 groups served as control groups and used traditional multimedia-based learning methods. Outcomes, including learning effectiveness, compliance, and satisfaction, were evaluated using questionnaires and tests.

resultsThe study groups exhibited significantly higher levels of compliance and satisfaction compared to the control groups. Specifically, the study groups exhibited significantly greater compliance in both pre-class preparation and post-class review activities (P < 0.05). During classroom teaching, Group 1 of the study group achieved significantly higher compliance than the control groups (P < 0.0001), while Group 2 of the study group showed significantly higher compliance than Group 1 (P < 0.001). In terms of seeking feedback and assistance, both Groups 1 and 2 of the study group had significantly higher compliance compared to Group 1 of the control group (P < 0.01, P < 0.001 respectively). Overall satisfaction was significantly higher in the study groups compared to the control groups (P < 0.0001), particularly with respect to course organization (P < 0.001, P < 0.05).

conclusionThe incorporation of ChatGPT into the surgical clerkship teaching model substantially enhances learner compliance and satisfaction, offering notable advantages in educational effectiveness.

Indexed as

Clinical ClerkshipGeneral SurgeryComputer-Assisted InstructionEducational MeasurementEducation, Medical, UndergraduateFemaleHumansMaleModels, EducationalProgram EvaluationStudents, MedicalArtificial intelligenceChatGPTComplianceLearning effectivenessMedical educationSatisfaction

Identifiers

PMID39731112
PMCPMC11674154

What OpenQuestion holds

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LicenceCC BY
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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.