Evidence map›Paper›PMID 42736913›Full record

ArticleHealthcare (Basel, Switzerland)2026

Exploring the Association Between AI-Derived Competency Scores and Nurse Retention.

Young Hye Song, Hyoung Ran Park, Mi Ran Kim, Hyeongju Ryu

Abstract read
In one paragraph

Article in Healthcare (Basel, Switzerland), 2026. 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
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.

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.

Young Hye SongDepartment of Nursing, Konkuk University Medical Center, Seoul 05030, Republic of Korea.ORCID 0000-0003-4582-4049
Hyoung Ran ParkDepartment of Nursing, Konkuk University Medical Center, Seoul 05030, Republic of Korea.
Mi Ran KimDepartment of Nursing, Konkuk University Medical Center, Seoul 05030, Republic of Korea.
Hyeongju RyuDepartment of Nursing and Research Institute for Biomedical & Health Science, Konkuk University, Chungju 27478, Republic of Korea.

Funding

Konkuk University
6 · The paper itself

Abstract

backgroundThis study explored the associations between artificial intelligence (AI)-derived competency scores and nurse retention outcomes among newly employed nurses. Evidence regarding the relationship between AI-based competency assessments, nurse performance, and long-term retention remains limited.

methodsData were collected from 156 newly employed nurses at hospitals in South Korea between March 2021 and August 2023. Retention status (retained vs. resigned) was specified as the dependent variable, while AI-derived competency scores served as independent variables in binary logistic regression analyses.

resultSelf-reflection was significantly associated with retention (

conclusionsThese findings provide preliminary evidence that selected AI-derived competency scores are associated with nurse retention outcomes. However, the findings should be interpreted cautiously because this study examined statistical associations rather than predictive performance, and further validation studies are required before AI-assisted competency assessments can be considered standalone decision-support tools for nurse recruitment.

Indexed as

AI-based recruitmentartificial intelligencecompetency assessmentemployee turnoverhealthcare workforcenewly employed nursesnurse retention

Identifiers

PMID42736913
PMCPMC13565578

What OpenQuestion holds

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Registered trials

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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.