Ruihua Jin

Background: Emotional intelligence is a core competency for nursing students; however, traditional nursing education faces challenges in fostering emotional intelligence, including curricular imbalances, limited teaching methods, and delayed assessment and feedback. The rapid development of generative artificial intelligence (GenAI) offers new possibilities for addressing the above issues; however, existing research has not yet thoroughly explored how nursing students learn emotional intelligence through GenAI or their experiences in this process. Objective: Based on the Unified Theory of Acceptance and Use of Technology (UTAUT) and the Teaching Pedagogical Content Knowledge (TPACK) framework, and using Goleman’s five-dimensional model of emotional intelligence as a reference, this study explores the experiences of undergraduate nursing students in using generative artificial intelligence for emotional intelligence learning, as well as the factors that facilitate this process. Method: This study employed a qualitative descriptive research design and Reflexive Thematic Analysis (RTA). Using purposive sampling, 19 undergraduate nursing students (from freshman to senior year) were selected for one-on-one semi-structured in-depth interviews. All participants had experience using GenAI tools (such as DeepSeek and Doubao) for communication practice, case discussions, or sentiment analysis. The interview data were analyzed using the RTA six-stage process, with NVivo 12.0 software used to assist with data management. Results: We identified and synthesized 238 initial codes into three core themes. Theme 1, “From Efficiency-Driven to Precise Empathy,” presents the differing descriptions of empathy-related experiences among students in different grade levels: lower-grade students more frequently reported efficiency-driven usage experiences; some middle-grade students described difficulties caused by technical barriers; and upper-grade students more often described the recognition of patients’ emotions in conjunction with clinical experience. Theme 2, “From Mimicking AI to Autonomous Expression,” focuses on students’ reported experiences related to social skills, including direct application, recognition and adjustment, autonomous expression, and the simulation gap that persisted throughout the process. Theme 3, “From Technical Frustration to Mental Rehearsal,” integrates experiences related to self-management, self-awareness, and self-motivation. The three themes correspond to different dimensions of emotional intelligence, with students in different grade levels reporting distinct experiential characteristics. Conclusion: Nursing undergraduates in different grades exhibit distinct experiential characteristics when using generative artificial intelligence to learn about emotional intelligence. Students’ reports repeatedly highlighted moderate tension around technological motivation, the ability to critically integrate technology, and the perceived value of technological friction. Based on these exploratory findings, we propose several preliminary educational implications—including grade-level-specific support, curriculum exploration, and education in critical technological literacy and ethics—for consideration in future research and practice.

Previous studies have reported that depressive symptoms were associated with diabetes and cardiovascular diseases(CVDs). However, the association between l

Neck pain is among the most prevalent musculoskeletal disorders affecting the general population. During the 2019 coronavirus disease 2019 (COVID-19) pandemic, students have increasingly resorted to online learning, requiring prolonged use of electronic devices. This study aimed to investigate the prevalence of and factors influencing neck pain during online learning. The study employed a cross-sectional design. Eligible participants were nursing students who had been receiving online instruction for a duration exceeding 3 months. To develop the study instrument, the researchers integrated the study objectives with insights from an extensive literature review. This process culminated in the creation of a comprehensive online questionnaire designed to capture relevant data. The prevalence of neck pain among students was analyzed for both the pre-COVID-19 and during COVID-19 periods. The chi-square test was utilized to compare the occurrence of neck pain between these 2 periods, while binary logistic regression was employed to examine the association between various influencing factors and neck pain. This study revealed that out of the 426 students who participated in the study, 391 were female (91.8%) and 35 were male (8.2%). The prevalence of neck pain during online learning (62.7%) was significantly higher than before online learning (37.3%) (P < .05). A significant correlation was also found between neck pain and learning while lying on a bed or table, duration of use of electronic devices, and exercising habits (P < .05). The prevalence of neck pain among students has significantly increased during the COVID-19 pandemic. Future research should focus on evaluating the long-term impact of distance learning on undergraduate students. Additionally, it is imperative to develop and implement targeted intervention programs based on the identified influencing factors to mitigate the prevalence of neck pain and alleviate neck discomfort.

Background: Patients suffering from major depressive disorder (MDD) commonly demonstrate lower performance across multiple cognitive domains. Cognitive impairment is an intrinsic characteristic of MDD status and is often influenced by genetic factors. microRNAs (miRNAs or miRs) have been shown to have important implications in the etiology of MDD. Therefore, we aimed to identify and analyze the impact of expression and genetic variation of miR-34b/c on cognitive dysfunction in MDD. Methods: First, we analyzed miR-34c-5p expression in 48 cases of MDD and 54 healthy controls in a Chinese population using qRT-PCR. We assessed the relationship between the level of miR-34c-5p expression and cognitive performance by Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) and the Trail Making Test (TMT). Second, in order to characterize allelic effects of miR-34b/c on cognitive performance in MDD patients, we performed genetic association analysis of single-nucleotide polymorphism (SNP) loci of the MIR34B/C genes with cognitive function in a second group consisting of 531 MDD patients and 267 healthy controls. Results: We found a significant negative correlation between the level of miR-34c-5p expression and both the language and delayed memory index scores in patients with MDD. We also found a significant positive correlation between the level of miR-34c-5p expression and the time required to complete tests A and B of the TMT. The interaction between the rs2187473 genotype and the disease was significant for both immediate memory and delayed memory. In the patient group, the rs2187473 CC genotype was significantly associated with higher performance on immediate memory (F = 6.683, p < 0.05) and delayed memory tasks (F = 4.221, p < 0.05). Conclusion: Our findings suggest that changes in miR-34c expression level have important impacts on cognitive function in patients with MDD. In particular, the polymorphism rs2187473 is a potential genetic risk factor for cognitive function in MDD, which may be of clinical use.