Media coverage of South Korea’s 2024 medical school quota increase: a structural topic modelling and sentiment analysis

Background Healthcare systems in developed countries face physician shortages, prompting many governments to expand medical school quotas. In South Korea, the 2024 quota increase triggered strong opposition from the medical profession and broader public debate. This study examines how this policy change was discussed in medical and pharmacy professional newspapers, with attention to dominant themes and sentiment patterns.Methods Articles published between January and June 2024 in six major professional newspapers were analysed. Structural topic modelling identified prevalent themes, and sentiment analysis assessed tonal differences across media types and over time.Results A total of 3854 articles were analysed. Medical newspapers published substantially more coverage and primarily structured the discourse around the ‘Doctors’ cluster, accounting for 52.1% of topics. Topic prevalence and sentiment varied over time and by outlet type. Medical newspapers consistently showed more negative sentiment, particularly regarding policy impacts and essential medical specialties. Educational discussions remained limited across both media types, especially with respect to training capacity and educational quality.Conclusions The quota increase was primarily constructed as a conflict between state and professional interests within political contexts, rather than as a long-term public policy debate. Both professional newspapers paid insufficient attention to the educational implications of the reform. Future discourse should prioritise the educational foundations essential for healthcare quality and public trust.

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Publication Details

Journal
Critical Public Health
Published
2026-04-27
DOI
https://doi.org/10.1080/09581596.2026.2660429
Primary Topic
Computational and Text Analysis Methods
Type
article
Field-Weighted Citation Impact
0.00

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article

Media coverage of South Korea’s 2024 medical school quota increase: a structural topic modelling and sentiment analysis

Youran Noh, Kyung‐Bok Son
Critical Public Health
Computational and Text Analysis Methods
article

Media coverage of South Korea’s 2024 medical school quota increase: a structural topic modelling and sentiment analysis

Youran Noh, Kyung‐Bok Son
article en

Abstract

Background Healthcare systems in developed countries face physician shortages, prompting many governments to expand medical school quotas. In South Korea, the 2024 quota increase triggered strong opposition from the medical profession and broader public debate. This study examines how this policy change was discussed in medical and pharmacy professional newspapers, with attention to dominant themes and sentiment patterns.Methods Articles published between January and June 2024 in six major professional newspapers were analysed. Structural topic modelling identified prevalent themes, and sentiment analysis assessed tonal differences across media types and over time.Results A total of 3854 articles were analysed. Medical newspapers published substantially more coverage and primarily structured the discourse around the ‘Doctors’ cluster, accounting for 52.1% of topics. Topic prevalence and sentiment varied over time and by outlet type. Medical newspapers consistently showed more negative sentiment, particularly regarding policy impacts and essential medical specialties. Educational discussions remained limited across both media types, especially with respect to training capacity and educational quality.Conclusions The quota increase was primarily constructed as a conflict between state and professional interests within political contexts, rather than as a long-term public policy debate. Both professional newspapers paid insufficient attention to the educational implications of the reform. Future discourse should prioritise the educational foundations essential for healthcare quality and public trust.

Critical Public HealthVol. 36(1)
Hanyang University (KR)
National Research Foundation of Korea
Openalex Percentile: Top 8%
Computational and Text Analysis Methods
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