Evidence map›Paper›PMID 34809416›Full record

ArticleEpidemiology and health2021

Gender differences in under-reporting hiring discrimination in Korea: a machine learning approach.

Jaehong Yoon, Ji-Hwan Kim, Yeonseung Chung, Jinsu Park, Glorian Sorensen, Seung-Sup Kim

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Epidemiology and health, 2021. 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

6 authors.

Jaehong YoonDepartment of Public Health Sciences, Graduate School of Korea University, Seoul, Korea.
Ji-Hwan KimDepartment of Public Health Sciences, Graduate School of Korea University, Seoul, Korea.
Yeonseung ChungDepartment of Mathematical Sciences, Korea Advanced Institute of Science and Technology, Daejeon, Korea.
Jinsu ParkDepartment of Mathematical Sciences, Korea Advanced Institute of Science and Technology, Daejeon, Korea.
Glorian SorensenDepartment of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Seung-Sup KimDepartment of Public Health Sciences, Graduate School of Korea University, Seoul, Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesThis study was conducted to examine gender differences in under-reporting hiring discrimination by building a prediction model for workers who responded "not applicable (NA)" to a question about hiring discrimination despite being eligible to answer.

methodsUsing data from 3,576 wage workers in the seventh wave (2004) of the Korea Labor and Income Panel Study, we trained and tested 9 machine learning algorithms using "yes" or "no" responses regarding the lifetime experience of hiring discrimination. We then applied the best-performing model to estimate the prevalence of experiencing hiring discrimination among those who answered "NA." Under-reporting of hiring discrimination was calculated by comparing the prevalence of hiring discrimination between the "yes" or "no" group and the "NA" group.

resultsBased on the predictions from the random forest model, we found that 58.8% of the "NA" group were predicted to have experienced hiring discrimination, while 19.7% of the "yes" or "no" group reported hiring discrimination. Among the "NA" group, the predicted prevalence of hiring discrimination for men and women was 45.3% and 84.8%, respectively.

conclusionsThis study introduces a methodological strategy for epidemiologic studies to address the under-reporting of discrimination by applying machine learning algorithms.

Indexed as

Machine LearningFemaleHumansMaleRepublic of KoreaSex FactorsMachine learningSocial discriminationSocial epidemiology

Identifiers

PMID34809416
PMCPMC8920741

What OpenQuestion holds

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LicenceCC BY
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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.