Background Promoting self-directed learning (SDL) among nursing undergraduates is crucial to meet the new requirements of the healthcare system and to adapt to online learning contexts during the COVID-19 pandemic. Therefore, identifying the classification features of SDL ability and developing targeted interventions are both critical. Professional identity (PI) may contribute to the cultivation of SDL ability, but their relationship remains relatively unknown. This study aimed to explore the subgroups of SDL ability and their differences in PI among nursing undergraduates during the COVID-19 pandemic. Methods A total of 2438 nursing undergraduates at four universities in China were enrolled in this cross-sectional study from November 2021 to February 2022. The Self-Directed Learning Scale of Nursing Undergraduates (SLSNU) and the Professional Identity Scale for Nursing Students (PISNS) were administered. A latent profile analysis was performed to explore SDL ability latent profiles. Multinomial logistic regression analysis was conducted to examine the predictors of profile membership, and a one-way analysis of variance was applied to compare the PI scores in each latent profile. Results Three latent profiles were identified and labeled ‘low SDL ability’ (n = 749, 30.7%), ‘low initiative of help-seeking’ (n = 1325, 54.4%) and ‘high SDL ability’ (n = 364, 14.9%). Multinomial logistic regression analysis suggested that nursing undergraduates who voluntarily chose a nursing major, had served as a student cadre, and had participated in clinical practicum were less likely to be included in the “low SDL ability” group. The average PI score was statistically different across the three profiles (F = 884.40, p < 0.001). Conclusion The SDL ability among nursing undergraduates was divided into three profiles, and results show that promoting PI may effectively foster SDL ability. This study highlights the importance of targeted interventions by considering their distinct SDL ability patterns, especially during the COVID-19 pandemic.
Aortic dissection (AD) is one of the most dangerous diseases of the cardiovascular system, which is characterized by acute onset and poor prognosis, while the pathogenesis of AD is still unclear and may affect or even delay the diagnosis of AD. Anchorage-dependent cell death (Anoikis) is a special mode of cell death, which is programmed cell death caused by normal cells after detachment from extracellular matrix (ECM)and has been widely studied in the field of oncology in recent years. In this study, we applied bioinformatics analysis, according to the results of research analysis and Gene Ontology (GO), as well as Kyoto Encyclopedia of Genes and Genomes (KEGG), finally found 3 anoikis-related genes (ARGs) based on machine learning. Then we further verified by receiver operating characteristic (ROC), gene set enrichment analysis (GSEA), gene set enrichment analysis (GSVA)and other methods. We hypothesize ARGs may be involved in the pathogenesis of AD through pathways such as oxidative stress, inflammatory response, and ECM. Therefore, we conclude that the ARGs can be an important factor in determining the diagnosis of AD.
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