Quantitative Research Critique: AI-Assisted Tailored Intervention for Nurse Burnout
Citation:
Baek, G., & Cha, C. (2025). AI-assisted tailored intervention for nurse burnout: A three-group randomized controlled trial. Worldviews on Evidence-Based Nursing, 22, e70003. https://doi.org/10.1111/wvn.70003
Level of Evidence:
According to the Johns Hopkins Hierarchy of Evidence (2022), this study qualifies as a Level I evidence due to its randomized controlled trial (RCT) design. This is the highest level of quantitative evidence, providing strong support for causal relationships due to minimized bias and control over confounding variables.
Study Design and Methods:
Baek and Cha conducted a three-arm RCT with n = 153 nurses randomized into an AI-tailored intervention group, a non-AI group, and a control group. Randomization and stratification were clearly described, strengthening internal validity. The study aimed to evaluate the effectiveness of an AI-assisted program in reducing burnout and improving resilience.
The sample size was justified via a power analysis (α = 0.05, power = 0.80), which indicated that at least 42 participants per group were needed to detect a moderate effect size. Since the final sample had over 50 participants per group, the sample size is statistically adequate to detect clinically meaningful differences.
Instrumentation and Validity:
Burnout was measured using the Maslach Burnout Inventory (MBI), a gold-standard tool with proven reliability (Cronbach’s alpha > 0.8) in nursing populations. The authors also used the Connor-Davidson Resilience Scale (CD-RISC) to measure psychological resilience. Both tools have established construct validity and internal consistency in similar populations, supporting the reliability of the outcome measures.
Statistical Analysis:
The authors used ANOVA and repeated measures ANCOVA to analyze within-group and between-group differences, adjusting for baseline scores. Effect sizes were reported using partial eta squared, which improves interpretability. The use of Bonferroni corrections addressed the risk of type I error due to multiple comparisons.
Statistical significance was set at p < 0.05, and findings showed the AI-assisted group had significant reductions in burnout scores at 8 weeks (p < 0.001), compared to both the non-AI and control groups. This provides robust evidence of intervention efficacy.
Findings and Interpretation:
The study concludes that AI-assisted, tailored interventions are superior to general interventions or no intervention in reducing nurse burnout and enhancing resilience. The authors interpreted their findings conservatively and acknowledged limitations such as the short follow-up period and single-country sampling, which may limit generalizability.
Limitations and Implications:
While methodologically rigorous, the study’s lack of long-term follow-up and potential for social desirability bias due to self-report measures are noted limitations. Also, the implementation feasibility in different healthcare systems is uncertain.
Despite this, the findings have clinical relevance for nurse leaders and policymakers, as AI can provide scalable, personalized mental health support to reduce burnout—a critical issue in the post-pandemic workforce.
Conclusion:
Baek and Cha’s (2025) RCT presents a high-quality, Level I evidence study with appropriate sample size, validated tools, and rigorous statistical analysis. Its findings support the implementation of AI-driven interventions in nursing practice, particularly in high-stress environments. Future research should explore long-term outcomes and replication in diverse clinical settings.
References:
Baek, G., & Cha, C. (2025). AI-assisted tailored intervention for nurse burnout: A three-group randomized controlled trial. Worldviews on Evidence-Based Nursing, 22, e70003. https://doi.org/10.1111/wvn.70003
Johns Hopkins Medicine. (2022). Johns Hopkins Nursing Evidence-Based Practice: Model and Guidelines (Appendix D: Evidence Level and Quality Guide). https://www.hopkinsmedicine.org/evidence-based-practice/_docs
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