Evidence map›Paper›PMID 32010007›Full record

ArticleFrontiers in psychology2019

Nomograms Predicting Self-Regulated Learning Levels in Chinese Undergraduate Medical Students.

Jun Yang, Guoyang Zhang, Runzhi Huang, Penghui Yan, Peng Hu, Lanting Huang, Tong Meng, Jie Zhang, Ruilin Liu, Ying Zeng and 12 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in psychology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.6field-weighted citation impact, top 17% of its field
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

6 citing papers in PubMed, 12 citations in OpenAlex.

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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

22 authors at 8 institutions in 1 country.

Jun YangShanghai Tongji Hospital, Tongji University School of Medicine, Shanghai, China.
Guoyang ZhangGraduate School of Education, Shanghai Jiao Tong University, Shanghai, China.
Runzhi HuangShanghai Tongji Hospital, Tongji University School of Medicine, Shanghai, China.
Penghui YanThe First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Peng HuThe First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Lanting HuangSchool of Finance, Henan University of Economics and Law, Zhengzhou, China.
Tong MengShanghai Tongji Hospital, Tongji University School of Medicine, Shanghai, China.
Jie ZhangKey Laboratory of Arrhythmias, Ministry of Education, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China.
Ruilin LiuShanghai Tongji Hospital, Tongji University School of Medicine, Shanghai, China.
Ying ZengTongji University School of Medicine, Tongji University, Shanghai, China.
Chunlan WeiTongji University School of Medicine, Tongji University, Shanghai, China.
Huixia ShenTongji University School of Medicine, Tongji University, Shanghai, China.
Miao XuanShanghai Tongji Hospital, Tongji University School of Medicine, Shanghai, China.
Qun LiOffice of Educational Administration, Naval Medical University, Shanghai, China.
Meiqiong GongGraduate School of Education, Shanghai Jiao Tong University, Shanghai, China.
Wenting ChenBasic Medical College of Fudan University, Shanghai, China.
Haifeng ChenOffice of Educational Administration, Bengbu Medical College, Bengbu, China.
Kaiyang FanOffice of Educational Administration, Naval Medical University, Shanghai, China.
Jing WuThe First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Zongqiang HuangThe First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Liming ChengShanghai Tongji Hospital, Tongji University School of Medicine, Shanghai, China.
Wenzhuo YangShanghai Tongji Hospital, Tongji University School of Medicine, Shanghai, China.
Tongji University · CNFirst Affiliated Hospital of Zhengzhou University · CNTongji Hospital · CNShanghai Jiao Tong University · CNBengbu Medical College · CNHenan University of Economic and Law · CNShanghai East Hospital · CNShanghai Medical College of Fudan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThe purpose of this study was to construct a multi-center cross-sectional study to predict self-regulated learning (SRL) levels of Chinese medical undergraduates.

methodsWe selected medical undergraduates by random sampling from five universities in mainland China. The classical regression methods (logistic regression and Lasso regression) and machine learning model were combined to identify the most significant predictors of SRL levels. Nomograms were built based on multivariable models. The accuracy, discrimination, and generalization of our nomograms were evaluated by the receiver operating characteristic curves (ROC) and the calibration curves and a high quality external validation.

resultsThere were 2052 medical undergraduates from five universities in mainland China initially. The nomograms constructed based on the non-overfitting multivariable models were verified by internal validation (C-index: learning motivation: 0.736; learning strategy: 0.744) and external validation (C-index: learning motivation: 0.986; learning strategy: 1.000), showing decent prediction accuracy, discrimination, and generalization.

conclusionComprehensive nomograms constructed in this study were useful and convenient tools to evaluate the SRL levels of undergraduate medical students in China.

Indexed as

medical undergraduatemulti-center cross-sectional studynomogramself-regulated learningvalidation

Identifiers

PMID32010007
PMCPMC6974523
OpenAlexW2998986146

What OpenQuestion holds

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LicenceCC BY
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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.