Evidence map›Paper›PMID 35085367›Full record

ArticlePloS one2022

Predicting the effectiveness of the online clinical clerkship curriculum: Development of a multivariate prediction model and validation study.

Naoto Kuroda, Anna Suzuki, Kai Ozawa, Nobuhiro Nagai, Yurika Okuyama, Kana Koshiishi, Masafumi Yamada, Makoto Kikukawa

Abstract readValidation Study
In one paragraph

Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
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  3. 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

8 authors.

Naoto KurodaDepartment of Pediatrics, Wayne State University, Detroit, Michigan, United States of America.ORCID 0000-0003-1977-4893
Anna SuzukiKyushu University School of Medicine, Fukuoka, Japan.
Kai OzawaThe Jikei University School of Medicine, Tokyo, Japan.
Nobuhiro NagaiFaculty of Medicine, Shimane University, Izumo, Japan.
Yurika OkuyamaAkita University School of Medicine, Akita, Japan.
Kana KoshiishiAsahikawa Medical University, Asahikawa, Japan.
Masafumi YamadaFaculty of Medicine, Shimane University, Izumo, Japan.
Makoto KikukawaDepartment of Medical Education, Kyushu University, Fukuoka, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Given scientific and technological advancements, expectations of online medical education are increasing. However, there is no way to predict the effectiveness of online clinical clerkship curricula. To develop a prediction model, we conducted cross-sectional national surveys in Japan. Social media surveys were conducted among medical students in Japan during the periods May-June 2020 and February-March 2021. We used the former for the derivation dataset and the latter for the validation dataset. We asked students questions in three areas: 1) opportunities to learn from each educational approach (lectures, medical quizzes, assignments, oral presentations, observation of physicians' practice, clinical skills practice, participation in interprofessional meetings, and interactive discussions with physicians) in online clinical clerkships compared to face-to-face, 2) frequency of technical problems on online platforms, and 3) satisfaction and motivation as outcome measurements. We developed a scoring system based on a multivariate prediction model for satisfaction and motivation in a cross-sectional study of 1,671 medical students during the period May-June 2020. We externally validated this scoring with a cross-sectional study of 106 medical students during February-March 2021 and assessed its predictive performance. The final prediction models in the derivation dataset included eight variables (frequency of lectures, medical quizzes, oral presentations, observation of physicians' practice, clinical skills practice, participation in interprofessional meetings, interactive discussions with physicians, and technical problems). We applied the prediction models created using the derivation dataset to a validation dataset. The prediction performance values, based on the area under the receiver operating characteristic curve, were 0.69 for satisfaction (sensitivity, 0.50; specificity, 0.89) and 0.75 for motivation (sensitivity, 0.71; specificity, 0.85). We developed a prediction model for the effectiveness of the online clinical clerkship curriculum, based on students' satisfaction and motivation. Our model will accurately predict and improve the online clinical clerkship curriculum effectiveness.

Indexed as

CurriculumModels, StatisticalMotivationPersonal SatisfactionClinical ClerkshipClinical CompetenceCross-Sectional StudiesData AccuracyEducation, DistanceEducation, MedicalFemaleHumansJapanMaleResearch DesignSensitivity and Specificity

Identifiers

PMID35085367
PMCPMC8794117

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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.