ArticleJournal of nursing management2026
Identification of Critical Variables and Critical Gap Variables in Hospital Nurses' Job Satisfaction During the Dynamic Adjustment Phase of COVID-19 Prevention in China: A Hybrid Machine Learning and Decision Analysis Approach.
Article in Journal of nursing management, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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.
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1 citing paper in PubMed.
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7 authors.
Funding
Abstract
purposeThis study aims to analyze the critical variables and gap variables affecting hospital nurses' job satisfaction and propose improvement strategies based on the knowledge domains of nursing decision-makers.
methodsThis study, conducted between September and October 2022 during the dynamic adjustment phase of COVID-19 prevention and control in China, was based on the McCloskey/Mueller Satisfaction Scale (MMSS) and developed a hybrid machine learning and decision analysis tool model. The random forest (RF) method was used to estimate the importance of each variable in the data, and the importance-performance analysis (IPA) was used to identify critical gap variables and propose improvement strategies.
resultsThe RF analysis (OOB error rate = 17.93%) identified "Decision-making" (C
conclusionsTo improve nurse job satisfaction and retention, hospital decision-makers and nursing departments should implement policies that enhance nurses' involvement in decision-making, particularly those with experience in pandemic-related healthcare challenges. Addressing these factors could foster a more supportive and resilient nursing work environment.
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