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Record W4408009831 · doi:10.3346/jkms.2025.40.e112

Healthcare Crisis in Korea and Its Impact on Medical Research: A PubMed Analysis (2022–2024)

2025· article· en· W4408009831 on OpenAlexaff
Soo Ick Cho, Jeong‐Moo Lee, Hyung Park, Jungyo Suh, Ro Woon Lee

Bibliographic record

VenueJournal of Korean Medical Science · 2025
Typearticle
Languageen
FieldMedicine
TopicDiverse Approaches in Healthcare and Education Studies
Canadian institutionsArtificial Intelligence in Medicine (Canada)
Fundersnot available
KeywordsHealth careMedicineMEDLINEPolitical science

Abstract

fetched live from OpenAlex

1/4 https://jkms.orgIn response to the Korea government's 2024 policy to increase the number of medical students, a nationwide resignation of residents and prolonged leave by medical students have disrupted healthcare services and medical education systems. 1,2The response to the government's policies has disrupted medical treatment and education at teaching hospitals and medical schools, leading to significant challenges in the sustainability of medical research as well as the healthcare system.A recent paper reported that the Journal of Korean Medical Science revealed in 2024 that the proportion of publications and submissions by domestic authors is declining. 3,4However, this analysis was limited to a single journal, restricting its generalizability.To see the broader impact of this event, we checked the change from 2022 to 2024 in the proportion of articles in PubMed that included authors from affiliations in Korea to figure out how the current nationwide healthcare crisis affected medical research.PubMed's Application Programming Interface was used to count articles published from January 2022 to December 2024 according to the month of publication (Supplementary Method). 5 Affiliations containing 'Korea,' 'Republic of Korea,' or 'South Korea' were identified.Papers were categorized as: 1) Korea-domestic study, where all authors were affiliated with Korea institutions, and 2) Korea-international collaborative study, where only some authors were affiliated with Korea.In 2022 and 2023, a total of 1,494,958 and 1,419,360 papers were published, of which 31,873 and 30,642 (2.13% and 2.10%) were Korea-domestic studies, and 11,878 and 11,883 (0.79% and 0.81%) were Korea-international collaborative studies, thus a total of 43,751 and 42,525 (2.93% and 2.91%) papers were from Korea affiliations (Table 1).In 2024, among 1,505,450 published papers, 30,473 (1.97%) were Korea-domestic studies, and 12,778 (0.83%) were Korea-international collaborative studies.While the total contribution of Koreaaffiliated publications decreased from 2.91% in 2023 to 2.79% in 2024, Korea-international collaborative studies were unaffected (0.81% to 0.83%).Furthermore, the monthly proportion of papers from Korea affiliations decreased after March 2024 (Table 1, Fig. 1).Comparing months from March through December for each year,

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.018
metaresearch head score (Gemma)0.018
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.269
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0180.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.007
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.163
GPT teacher head0.497
Teacher spread0.334 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2025
Admission routes1
Has abstractyes

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