Evidence map›Paper›PMID 40417174›Full record

ArticleJournal of medical education and curricular development

Exploring the Essential Competencies for Medical Education in the Post-COVID-19 Pandemic Era: A Mixed-Methods Investigation at a Japanese Medical School.

Misaki Onodera, Zaiya Takahashi, Hajime Kasai, Hidetaka Yokoh, Kiyoshi Shikino, Yasuhiko Kimura, Ikuo Shimizu, Kazuyo Yamauchi, Mayumi Asahina, Shoichi Ito

Abstract read
In one paragraph

Article in Journal of medical education and curricular development. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

10 authors.

Misaki OnoderaDepartment of Medical Education, Graduate School of Medicine, Chiba University, Chiba, Japan.ORCID https://orcid.org/0009-0002-0126-9143
Zaiya TakahashiDepartment of Community-Oriented Medical Education, Graduate School of Medicine, Chiba University, Chiba, Japan.ORCID https://orcid.org/0000-0002-8520-9502
Hajime KasaiDepartment of Medical Education, Graduate School of Medicine, Chiba University, Chiba, Japan.ORCID https://orcid.org/0000-0002-6759-2026
Hidetaka YokohDepartment of Medical Education, Graduate School of Medicine, Chiba University, Chiba, Japan.ORCID https://orcid.org/0000-0002-6784-3243
Kiyoshi ShikinoDepartment of Community-Oriented Medical Education, Graduate School of Medicine, Chiba University, Chiba, Japan.ORCID https://orcid.org/0000-0002-3721-3443
Yasuhiko KimuraCenter for Innovation in Medical and Dental Education, Kagoshima University, Kagoshima, Japan.ORCID https://orcid.org/0009-0009-6844-7347
Ikuo ShimizuDepartment of Medical Education, Graduate School of Medicine, Chiba University, Chiba, Japan.ORCID https://orcid.org/0000-0002-6731-7104
Kazuyo YamauchiDepartment of Community-Oriented Medical Education, Graduate School of Medicine, Chiba University, Chiba, Japan.ORCID https://orcid.org/0000-0001-6587-2393
Mayumi AsahinaDepartment of Medical Education, Graduate School of Medicine, Chiba University, Chiba, Japan.ORCID https://orcid.org/0000-0003-1102-4334
Shoichi ItoDepartment of Medical Education, Graduate School of Medicine, Chiba University, Chiba, Japan.ORCID https://orcid.org/0000-0003-3659-6152

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The coronavirus disease 2019 (COVID-19) pandemic has led to considerable advances in medical education through technological integration. The crisis generated by the pandemic in medical practice, education, and evolving technology has led to changes in the skills of medical professionals. This study aimed to examine the competencies required of medical students in the post-pandemic era. Methods: We conducted 2 mixed-methods studies. Study 1 explored medical students' necessary competencies after the COVID-19 pandemic. We conducted group work with faculty members and students from the Chiba University School of Medicine, captured proposed competencies, discussed them, and qualitatively analyzed the group work data using content analysis to extract the competencies. Study 2 was a secondary data analysis that compared the categories identified in Study 1 with the competencies required prior to the COVID-19 pandemic, which were extracted from the websites of all 82 medical schools and colleges in Japan, to identify the differences in competencies before and after the pandemic. Results: Study 1 resulted in the identification of 12 categories and 62 subcategories. The results of Study 2 showed that the increased occurrence of competencies was related to the utilization of information and communication technology (ICT) and artificial intelligence (AI), self-management, information gathering and explanation, liberal arts and generic skills, and exploring medicine and medical care/research presentations. The prevalence rates of these factors prior to the COVID-19 outbreak were 17.1%, 28.0%, 39.0%, 41.5%, and 48.8%, respectively. Conclusions: Competency-based medical education in ICT, self-management, and medical exploration has become increasingly important after the pandemic. Therefore, it is necessary to develop an educational curriculum to enable medical students to acquire these competencies. The study findings contribute to the literature on medical education and offer valuable insight into setting effective academic goals and designing suitable curricula for undergraduate medical students in the post-pandemic era.

Indexed as

competency-based medical educationcontent analysisCOVID-19information communication and technologyself-management

Identifiers

PMID40417174
PMCPMC12099140

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

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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.