Evidence map›Paper›PMID 39862129›Full record

ArticleMedical education online2025

Exploring the significance of medical humanities in shaping internship performance: insights from curriculum categories.

Chao Ting Chen, Anna Y Q Huang, Po-Hsun Hou, Ji-Yang Lin, His-Han Chen, Shiau-Shian Huang, Stephen J H Yang

Abstract read
In one paragraph

Article in Medical education online, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Chao Ting ChenDepartment of Medical Education, Taipei Veterans General Hospital, Taipei, Taiwan.
Anna Y Q HuangComputer Science and Information Engineering, National Central University, Taoyuan, Taiwan.
Po-Hsun HouDepartment of psychiatry, Taichung Veterans General Hospital, Taichung, Taiwan.
Ji-Yang LinComputer Science and Information Engineering, National Central University, Taoyuan, Taiwan.
His-Han ChenDepartment of Psychiatry, Yang Ji Mental Hospital, Keelung, Taiwan.
Shiau-Shian HuangDepartment of Medical Education, Taipei Veterans General Hospital, Taipei, Taiwan.ORCID 0000-0001-8647-1871
Stephen J H YangComputer Science and Information Engineering, National Central University, Taoyuan, Taiwan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMedical Humanities (MH) curricula integrate humanities disciplines into medical education to nurture essential qualities in future physicians. However, the impact of MH on clinical competencies during formative training phases remains underexplored. This study aimed to determine the influence of MH curricula on internship performance.

methodsThe academic records of 1364 medical students across 8 years of admission cohorts were analyzed. Performance in basic sciences, clinical skills, MH, and internship rotations were investigated, including the subgroup analysis of MH curricula. Ten-fold cross-validation machine learning models (support vector machines, logistic regression, random forest) were performed to predict the internship grades. In addition, multiple variables regression was done to know the independent impact of MH on internship grades.

resultsMH showed the important roles in predicting internship performance in the machine learning model, with substantially reduced predictive accuracy after excluding MH variables (e.g. Area Under the Curve (AUC) declining from 0.781 to 0.742 in logistic regression). Multiple variables regression revealed that MH, after controlling for the scores of other subjects, has the highest odds ratio (OR: 1.29,

conclusionMH had the strongest predictive association with clinical competence during formative internship training, beyond basic medical sciences. Integrating humanities merits greater prioritization in medical curricula to nurture skilled, compassionate physicians. Further research should investigate the longitudinal impacts of humanities engagement.

Indexed as

Clinical CompetenceCurriculumHumanitiesInternship and ResidencyEducational MeasurementFemaleHumansMachine LearningMaleStudents, Medicalinternship performancemachine learning regressionmedical educationMedical humanitiesmultiple logistic regression

Identifiers

PMID39862129
PMCPMC11770856

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