Evidence map›Paper›PMID 36503200›Full record

ArticleJournal of educational evaluation for health professions2022

Possibility of independent use of the yes/no Angoff and Hofstee methods for the standard setting of the Korean Medical Licensing Examination written test: a descriptive study.

Do-Hwan Kim, Ye Ji Kang, Hoon-Ki Park

Abstract read
In one paragraph

Article in Journal of educational evaluation for health professions, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–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 synthesis or guideline pooled it.

  1. Pooled it
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  3. 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

3 authors.

Do-Hwan KimDepartment of Medical Education, Hanyang University College of Medicine, Seoul, Korea.
Ye Ji KangDepartment of Medical Education, Hanyang University College of Medicine, Seoul, Korea.
Hoon-Ki ParkDepartment of Family Medicine, Hanyang University College of Medicine, Seoul, Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThis study aims to apply the yes/no Angoff and Hofstee methods to actual Korean Medical Licensing Examination (KMLE) 2022 written examination data to estimate cut scores for the written KMLE.

methodsFourteen panelists gathered to derive the cut score of the 86th KMLE written examination data using the yes/no Angoff method. The panel reviewed the items individually before the meeting and shared their respective understanding of the minimum-competency physician. The standard setting process was conducted in 5 rounds over a total of 800 minutes. In addition, 2 rounds of the Hofstee method were conducted before starting the standard setting process and after the second round of yes/no Angoff.

resultsFor yes/no Angoff, as each round progressed, the panel’s opinion gradually converged to a cut score of 198 points, and the final passing rate was 95.1%. The Hofstee cut score was 208 points out of a maximum 320 with a passing rate of 92.1% at the first round. It scored 204 points with a passing rate of 93.3% in the second round.

conclusionThe difference between the cut scores obtained through yes/no Angoff and Hofstee methods did not exceed 2% points, and they were within the range of cut scores from previous studies. In both methods, the difference between the panelists decreased as rounds were repeated. Overall, our findings suggest the acceptability of cut scores and the possibility of independent use of both methods.

Indexed as

Clinical CompetenceEducational MeasurementHumansLicensureEducational measurementHumansPhysiciansRepublic of KoreaUndergraduate medical education

Identifiers

PMID36503200
PMCPMC9845067

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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.