Evidence map›Paper›PMID 35255618›Full record

ArticleKorean journal of medical education2022

The validity of the assignment book-based structured interview in predicting academic performance in medical schools: a retrospective cohort study.

Hee Jae Lee, Sook-Won Ryu, Jun Yeon Won, Hee-Won Park

Open access · goldAbstract read
In one paragraph

Article in Korean journal of medical education, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact, top 97% 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

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

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

4 authors at 1 institution in 1 country.

Hee Jae LeeDepartment of Medicine, Kangwon National University School of Medicine, Chuncheon, Korea.
Sook-Won RyuDepartment of Medicine, Kangwon National University School of Medicine, Chuncheon, Korea.
Jun Yeon WonDepartment of Medicine, Kangwon National University School of Medicine, Chuncheon, Korea.
Hee-Won ParkDepartment of Medicine, Kangwon National University School of Medicine, Chuncheon, Korea.
Kangwon National University · KR

Funding

Kangwon National University 520170441
6 · The paper itself

Abstract

purposeStructured interviews have become essential in the medical schools admission selection because structured interviews predict academic achievement after admission. The purpose of this study was to determine validity and fairness of the new structural interview technique, assignment book-based structured interview (ABSI), in predicting future academic achievement of the medical students.

methodsThe validity of this new interview technique and academic achievement was evaluated based on the data of all the applicants and successful applicants who applied for on-time admission between the year 2011 and 2014.

resultsThe ABSI technique showed a significant correlation and predictive validity for academic achievement in the medical school. The retention group received significantly lower T-scores of ABSI compared with the superior student group.

conclusionThe results indicate that ABSI is a feasible, reliable, fair and valid admission selection tool. The ABSI may be meaningful and fair method for predicting academic achievements, and it could be incorporated as one of the contents in the multiple mini-interview.

Indexed as

Academic PerformanceStudents, MedicalBooksHumansRetrospective StudiesSchool Admission CriteriaSchools, MedicalAcademic successAssignment book-basedCollege admission testStructured interviewValidation study

Identifiers

PMID35255618
PMCPMC8906922
OpenAlexW4220742473

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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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.