Evidence map›Paper›PMID 39981583›Full record

Trial reportWorldviews on evidence-based nursing2025

AI-Assisted Tailored Intervention for Nurse Burnout: A Three-Group Randomized Controlled Trial.

Gumhee Baek, Chiyoung Cha

Abstract readRandomized Controlled Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in Worldviews on evidence-based nursing, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 3 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

13 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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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

2 authors.

Gumhee BaekCollege of Nursing, Ewha Research Institute of Nursing Science, Ewha Womans University, Seoul, South Korea.ORCID https://orcid.org/0000-0003-1999-0158
Chiyoung ChaCollege of Nursing, Ewha Research Institute of Nursing Science, Ewha Womans University, Seoul, South Korea.ORCID https://orcid.org/0000-0003-0115-1348

Funding

National Research Foundation of Korea 2021R1A2C2008166National Research Foundation of Korea RS-2024-00351194
6 · The paper itself

Abstract

backgroundHigh-stress environments, heavy workloads, and the emotional demands of patient care, which are common challenges faced by nurses, are factors that can lead to burnout. Shift work can make traditional burnout interventions costly and difficult to implement. Artificial intelligence (AI) could offer solutions that are less constrained by time, resources, and labor.

aimTo investigate the effectiveness of an AI-assisted intervention in reducing nurse burnout.

methodsA single-blind, three-group, randomized controlled trial of 120 nurses (40 per group) was conducted from June 2023 to July 2023. The AI-assisted tailored intervention included two 2-week programs, delivering one of four programs to the intervention group: mindfulness meditation, acceptance commitment therapy, storytelling and reflective writing, or laughter therapy. The experimental group received tailored programs based on demographic and work-related characteristics, job stress, stress response, coping strategy, and burnout dimensions (client-related, personal, and work-related). Control Group 1 self-selected their programs, while Control Group 2 was provided with online information on burnout reduction. Primary outcomes, client-related, personal, and work-related burnout, were measured at baseline, week 2, and week 4. Secondary outcomes, job stress, stress responses, and coping strategies, were assessed at baseline and week 4. Data were analyzed using ANOVA, repeated measures ANOVA, and the Scheffé test for post hoc analysis.

resultsThe experimental group showed significant reductions in client-related burnout (F = 7.725, p = 0.001) and personal burnout (F = 10.967, p < 0.0001) compared to the other groups. Significant effects of time and time × group interactions were observed for client-related and personal burnout, with time effects noted for work-related burnout. Stress response reduction was highest in Control Group 1, followed by the experimental group and Control Group 2 (F = 3.07, p = 0.017). LINKING EVIDENCE TO ACTION: AI algorithms could provide tailored programs to mitigate nurse burnout, particularly in client-related and personal burnout. Reducing nurse burnout could contribute to the quality of care.

trial registrationThis trial is registered with the Clinical Research Information Service (KCT0008546).

Indexed as

Artificial IntelligenceBurnout, ProfessionalNursesAdaptation, PsychologicalAdultFemaleHumansMaleMiddle AgedSingle-Blind MethodAIartificial intelligenceburnoutnursesrandomized controlled trialRCTRN

Identifiers

PMID39981583
PMCPMC11843407

What OpenQuestion holds

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LicenceCC BY-NC
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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.