{"paper_id":"024ff8fe-c594-44d7-8bd9-e14c33c63394","body_text":"A Qualitative Study on the Willingness and Application Demands of Clinical Nurses in the use of Large Language Models | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Qualitative Study on the Willingness and Application Demands of Clinical Nurses in the use of Large Language Models Yingjie Guo, Jiantao Guo, Yi Qiu, Shengjuan Yan, Ying Wang, Qingwei Zhu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8471430/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background: This study aims to investigate the willingness of clinical nurses to adopt large language models (LLMs) and their specific application requirements. The findings are intended to provide a scientific basis for promoting the rational integration of LLMs into nursing practice and improving the efficiency of clinical nursing work. Methods: A descriptive qualitative research design was adopted. Using the maximum variation purposive sampling method, clinical nurses from multiple Class-A tertiary hospitals were recruited as participants in Hohhot City, Inner Mongolia between June and August 2025. Semi-structured interviews were conducted offline. After transcribing the interview recordings, an in-depth data analysis was performed using Colaizzi's seven-step approach. Results: A total of 16 participants were included in the study. Four main themes and twelve sub-themes emerged: Cognition and Acceptance (Clinical nurses generally have limited understanding of LLMs; most hold positive attitudes toward their potential use, while a minority express skepticism); Application Scenario Requirements (Including nursing education, routine care and repetitive tasks, nursing information collection and system integration, and nursing research); Learning and Training Willingness and Needs (Participants demonstrated varying levels of interest in learning about LLMs and differing training needs); Concerns and Barriers (Technical limitations of LLMs, concerns from the perspective of clinical nurses, and patient-related apprehensions were identified). Conclusion: Clinical nurses exhibit both interest in and reservations about the application of LLMs. Future efforts should focus on enhancing the technical safety and reliability of LLMs, developing functional modules tailored to nursing practice, and implementing targeted, stratified training programs. These measures will increase the acceptance and utilization of LLMs among clinical nurses, ultimately contributing to improved nursing efficiency and quality. Introduction The development of artificial intelligence (AI) technology in the field of nursing has spanned nearly four decades, and it has demonstrated tremendous potential in enhancing the quality of clinical nursing, advancing the development of nursing education, and supporting nursing scientific research. In recent years, with the rapid advancement of AI technology, Large Language Models (LLMs) have emerged. LLMs are a category of language models based on the Transformer architecture, with parameter scales ranging from billions to trillions or even higher. Relying primarily on deep learning technology, these models undergo large-scale data training and are capable of performing complex tasks such as natural language processing and human-computer interaction [1]. ChatGPT, launched by OpenAI in the United States, attracted widespread attention in the medical and nursing fields immediately after its release. Additionally, localized LLMs in China, such as ERNIE Bot, DeepSeek, and Doubao, have gradually come to the fore. LLMs are now at the forefront of medical AI with immense potential to improve the efficiency and effectiveness of clinical, educational and research work, but they require extensive validation and further development to overcome technological weaknesses. China's \"14th Five-Year Plan\" clearly identifies the integration of AI technology and the field of life and health as strategic priorities. The Chinese government will provide full support in terms of funding, projects, talents and other aspects. Currently, research on LLMs in the medical field is relatively extensive, but it mostly focuses on literature reviews and current situation surveys [2-7]. In China, there are only two qualitative studies on nursing students' experience of using AI tools [8, 9], while qualitative research targeting clinical nurses remains insufficient. Therefore, it is particularly important to understand clinical nurses' willingness to apply LLMs, their current needs, and their usage experience. This study employs qualitative interviews to conduct in-depth analysis and interpretation of clinical nurses' willingness to use LLMs, application needs, learning and training needs, as well as concerns and worries. The aim is to provide a basis for nursing managers to develop training programs, promote the popularization of LLMs in the nursing field, and thereby improve the efficiency of clinical nursing work. Participants and Methods 1.1 Study Participants From June to August 2025, this study selected clinical nurses from multiple Grade A tertiary hospitals in Hohhot, Inner Mongolia Autonomous Region as research participants, and conducted offline semi-structured interviews. The maximum variation purposive sampling method was adopted to ensure the representativeness and typicality of the interview samples, thereby obtaining as rich data as possible. Inclusion Criteria In-service nurses holding a nurse practitioner qualification certificate; Nurses who are on duty and willing to participate in this study; Participants who have been informed of the study details and consented voluntarily. Exclusion Criteria Nurses who are in probation, internship, or receiving standardized training for further study. This study followed the principle of information saturation, which means that data collection was stopped when the collected information was duplicated and no new themes emerged from data analysis. 1.2 Research Methods 1.2.1 Development of the Interview Outline This study was conducted in accordance with the Standards for Reporting Qualitative Research (SRQR) checklist [ 10 ]. To develop the interview outline, the research team first conducted a systematic literature review centered on the study objectives, and initially drafted the outline by integrating the outcomes of group discussions. To verify the practicality and validity of the draft outline, a pilot interview was conducted with 2 clinical nurses. Based on the feedback from the pilot interview and suggestions from the interviewees, the draft was revised and refined to form the final formal interview outline, which included the following questions: (1) Have you heard of or do you have any understanding of Large Language Models (LLMs)? What is your first impression of LLMs? (2) In your opinion, what potential application values do LLMs have in the medical field? (3) Are you willing to use LLMs in your work? Please explain the reasons. (4) What factors do you think will affect your willingness to use LLMs? (5) Which specific nursing tasks do you think LLMs can help you accomplish? (6) In your view, what advantages might the application of LLMs in nursing work bring, or what challenges might it face? (7) Do you hope the hospital will offer relevant training on LLMs? Please explain the reasons. (8) What suggestions or expectations do you have for the future development of LLMs in the nursing field? (9) Is there any other information you would like to add? 1.2.2 Data Collection Methods All members of the research team received systematic specialized training in qualitative research methodology, equipping them with a solid theoretical foundation and rich interview experience. This study adopted face-to-face in-depth interviews, which were conducted in quiet and undisturbed demonstration classrooms or meeting rooms. On-site note-taking and audio recording were combined to ensure the completeness and accuracy of the data. Before each interview, researchers provided interviewees with a detailed explanation of the interview’s purpose, content, and duration. They also informed interviewees that the entire process would be audio-recorded, while promising that the recording content would only be used for academic research and that the principle of confidentiality would be strictly followed. Interviews were conducted in strict accordance with the outline, but the order of questions and questioning strategies were flexibly adjusted based on the interviewees’ responses. Researchers listened attentively and created an open atmosphere to encourage interviewees to express themselves fully and provide timely feedback. For ambiguous expressions or content that could be further explored, researchers used repeated follow-up questions to clarify the true intentions of interviewees, and also recorded non-verbal information such as facial expressions and body movements. The duration of a single interview was controlled between 20 and 30 minutes. When no new themes emerged, the interview was terminated in accordance with the theory of information saturation. Within 48 hours after the interview, the audio recording was transcribed verbatim. After repeated proofreading, the transcript was returned to the interviewee for confirmation to ensure the reliability and validity of the research data. 1.2.3 Data Analysis Methods and Quality Control Interview participants were coded using English letters, and transcribed text data were stored and organized. The Colaizzi 7-step analysis method was adopted for data analysis [ 11 ], with specific steps as follows: (1) Carefully read the transcribed materials to form a general understanding of all statements; (2) Mark and extract statements relevant to this study; (3) Code the extracted statements; (4) Summarize the codes, identify common concepts, and form themes; (5) Describe each theme in detail; (6) Determine the final themes after analyzing similar viewpoints; (7) Feed back the final themes to the interview participants for verification. NVivo 14 software was used to assist in data analysis. Two researchers independently analyzed and coded the interview data to form themes. Meanwhile, the research process was documented by writing detailed memos and reflective journals to avoid bias. When disputes arose, they were resolved through discussions among members of the research team, so as to enhance the scientificity and reliability of the research results. 1.2.4 Ethical Principles This study has adhered to the Declaration of Helsinki and obtained approval from the Ethics Committee of a Grade A tertiary hospital in Inner Mongolia, and adheres to the ethical principles of respect, beneficence, justice, and non-maleficence. Before the interview, the research team provided interviewees with a detailed explanation of the purpose and significance of the study, and required participants to sign an informed consent form. Participants had the right to withdraw from the study at any time without providing a reason. Results A total of 16 clinical nurses were interviewed in this study. The duration of each individual interview ranged from 20 to 30 minutes, with an average duration of 23 minutes. After transcription, the interview recordings amounted to a total of 60,854 Chinese characters. The general demographic information of the interviewees is detailed in Table 1 . Through data analysis, this study summarized 4 core themes and 12 sub-themes. Table 1 General Information of Respondents (n = 16) Code Age (Years) Department Working Experience (Years) Nurse Grade Educational Background Professional Title Position N1 44 Respiratory Medicine 23 N4 Bachelor's Degree Associate Chief Nurse Head Nurse N2 41 Respiratory Medicine 16 N4 Bachelor's Degree Associate Chief Nurse None N3 36 Cardiology Department 14 N3 Bachelor's Degree Nurse Practitioner Head Nurse N4 45 Cardiology Department 21 N3 Bachelor's Degree Associate Chief Nurse None N5 33 Cardiology Department 10 N3 Bachelor's Degree Nurse Practitioner None N6 46 Neurosurgery Department 26 N4 Bachelor's Degree Chief Nurse Head Nurse N7 38 Neurosurgery Department 15 N4 Bachelor's Degree Chief Nurse None N8 35 Gynecology Department 11 N4 Master's Degree Associate Chief Nurse None N9 40 Gynecology Department 19 N3 Bachelor's Degree Nurse Practitioner Head Nurse N10 24 Orthopedics Department 2 N1 Bachelor's Degree Staff Nurse None Table 1 (Continued) General Information of Respondents (n = 16) Code Age (Years) Department Working Experience (Years) Nurse Grade Educational Background Professional Title Position N12 42 Thoracic Surgery Department 16 N4 Bachelor's Degree Associate Chief Nurse Head Nurse N13 37 Intensive Care Unit (ICU) 14 N2 Junior College Degree Nurse Practitioner None N14 33 Emergency Department 9 N3 Bachelor's Degree Nurse Practitioner None N15 36 Neonatology Department 11 N3 Master's Degree Nurse Practitioner None N16 32 Emergency Department 9 N3 Bachelor's Degree Nurse Practitioner None Table 1 (Continued) General Information of Respondents (n = 16) Code Age (Years) Department Working Experience (Years) Nurse Grade Educational Background Professional Title Position N12 42 Thoracic Surgery Department 16 N4 Bachelor's Degree Associate Chief Nurse Head Nurse N13 37 Intensive Care Unit (ICU) 14 N2 Junior College Degree Nurse Practitioner None N14 33 Emergency Department 9 N3 Bachelor's Degree Nurse Practitioner None N15 36 Neonatology Department 11 N3 Master's Degree Nurse Practitioner None N16 32 Emergency Department 9 N3 Bachelor's Degree Nurse Practitioner None 2.1 Theme 1: Cognition and Acceptance Level 2.1.1 Overall Low Level of Cognition of Large Language Models Among Clinical Nurses With the growing popularity of LLMs, terms such as \"ChatGPT\" and \"DeepSeek\" have gradually entered public awareness. However, interview results revealed that some interviewees’ understanding of LLMs was limited to media reports or conversations with colleagues; while others reported having attempted to use LLMs in their work to assist with certain tasks, their grasp of relevant technical knowledge remained insufficient. N4 mentioned, \"I have heard of and learned a bit about LLMs, but I haven’t studied them in depth—only understanding superficial content.\" N13 noted, \"Nowadays, people have heard of LLMs, but I think very few actually apply them, especially in our professional circle, where access is quite limited.\" N14 stated, \"I know something about DeepSeek and ChatGPT, but it was only through your introduction just now that I learned these are collectively called Large Language Models.\" 2.1.2 Most Clinical Nurses Hold a Positive Attitude Toward Large Language Models Overall, interviewees expressed a positive attitude toward their experience with LLMs, and they generally described their first impression of LLMs as \"easy to use,\" \"convenient,\" and \"amazing.\" N1 stated, \"I have heard of this technology. Before I learned much about it, I wondered if it was really that good. But after trying it, I found it is indeed good—quite amazing.\" N14 mentioned, \"I think it is both easy to use and convenient. Especially after DeepSeek was launched, I just tried it recently. Whether I was searching for travel guides when going on a trip or writing speech drafts, it gave me a good experience.\" 2.1.3 A Minority of Clinical Nurses Hold a Skeptical Attitude Toward Large Language Models A small number of interviewees still maintained a skeptical attitude toward LLMs. They argued that the content generated by LLMs had not met their expectations, and meanwhile expressed concerns about the accuracy and safety of LLMs. N2 pointed out, \"In fact, LLMs can generate a lot of sophisticated or policy-oriented content, but there is actually a certain gap when it comes to practical application in my work with patients.\" N11 stated, \"I feel it may not be accurate or safe enough. When it comes to issues involving patients and human lives, I believe we need to rely on human professional judgment—I don’t trust LLMs 100%.\" 2.2 Theme 2: Application Scenario Requirements 2.2.1 Nursing Education In the field of nursing education, interviewees pointed out that LLMs can not only assist clinical nurses in creating courseware and PPTs, but also leverage their rich data resources and human-like reasoning capabilities to enrich clinical nurses’ professional knowledge, enhance clinical skills, and improve communication abilities. N13 stated, \"Since our hospital is a teaching hospital, LLMs are very practical in teaching—they can help create PPTs for students.\" N16 proposed, \"For example, after I just participated in the rescue of a patient with a respiratory disease, I hope LLMs can generate all knowledge related to respiratory rescue, which will help expand our knowledge scope better.\" N8 indicated, \"In terms of nursing education, applications like virtual patients are quite effective for new nurses and nursing students.\" 2.2.2 Daily Care and Mechanical Tasks Daily care and mechanical, repetitive nursing operations occupy a large amount of clinical nurses’ working time, leading to a common feeling among nurses that they are \"busy with trivial tasks and unable to focus on professional work.\" Most interviewees expressed the expectation that LLMs could be integrated with nursing robots in the future to take on tasks related to daily care and mechanical work. N4 pointed out, \"For example, ward rounds, measuring vital signs, replacing intravenous fluids, changing bed sheets and quilt covers, and helping patients turn over and pat their backs—these tasks actually consume a lot of human resources.\" N12 mentioned, \"For some mechanical tasks that do not require humanistic care, such as reminding patients of the time for imaging examinations or blood drawing schedules, we can use LLMs to preset the time and send regular reminders to them.\" N13 stated, \"Since tasks like medication retrieval and drug collection follow fixed procedures, LLMs can be programmed manually to perform these tasks; it can completely replace humans for fixed, mechanical work.\" 2.2.3 Nursing Information Collection and System Integration Traditional manual entry of nursing information is not only time-consuming but also prone to errors. The application of LLMs in nursing information collection and system integration not only improves clinical efficiency and accuracy but also further promotes nursing to return to its \"patient-centered\" essence [ 12 ]. N3 stated, \"If the system (an LLM-integrated nursing system) detects an increase in a patient’s body temperature, it will pop up an abnormal alert and provide relevant nursing interventions—I find this quite convenient.\" N15 pointed out, \"In our neonatology department, there are actually many trivial nursing tasks. If there is an intelligent system to monitor completed and pending tasks, I believe the quality of clinical nursing will be higher; this is much more effective than relying on nurses’ memory to keep track of these tasks every day.\" 2.2.4 Nursing Research Clinical nurses usually face the dual work pressure of \"research-clinical practice\". With multi-task processing capabilities such as natural language understanding, text generation, and data analysis, LLMs enable clinical nurses to access the latest research progress and organize analytical ideas through low-cost LLM tools, thereby providing assistance in research design. N3 pointed out, \"As a data storage tool, LLMs are actually far more powerful than the human brain. They contain a vast amount of information and materials, which we can retrieve easily.\" N13 proposed, \"Sometimes I have ideas in my mind but cannot summarize them accurately in words. By using ChatGPT, I can express the desired results precisely and comprehensively, which helps me organize my thoughts better.\" N14 stated, \"I don’t have a good understanding of research content, and at the same time, I’m engaged in clinical work. It seems I don’t have enough time for research either. So I think if LLMs can be integrated with research—teaching us how to identify research questions in clinical work and write research papers—it would be very helpful.\" 2.3 Theme 3: Willingness and Needs for Learning and Training 2.3.1 Differences in Learning Willingness Most interviewees held an open attitude toward emerging technologies and demonstrated a strong willingness to participate in training. However, a small number of interviewees maintained a negative stance toward training: they were more focused on content that could directly address practical work problems, considered training on LLMs unnecessary in personal usage scenarios, and worried that training would take up rest time, thereby further increasing their workload. N1 stated, \"We are still exploring ways to apply LLMs more extensively in clinical work to leverage their functions. However, the specific methods of application and the extent to which they can be utilized require further learning and exploration on our part.\" N15 mentioned, \"I would prefer to receive systematic training. I believe that only through systematic learning can we make good use of a new tool; otherwise, we can only gain a superficial understanding of it.\" N3 pointed out, \"I hope to learn about the functions that have already been integrated into the hospital’s current system, as well as potential risks associated with them. But for tools like ChatGPT, specialized training may not be necessary, since their use depends on personal preferences or habits.\" N8 indicated, \"If the hospital plans to introduce this tool, one or two training sessions would be sufficient. If it does not plan to introduce it, there is no need for training—those who need it can learn independently. For us, additional training really becomes a burden (with a bitter laugh).\" 2.3.2 Differences in Training Needs Clinical nurses exhibit differences in age, educational background, and computer literacy, which result in varied training needs for LLMs. Interview findings revealed that interviewees’ training needs primarily focused on basic concepts, usage methods, and operational skills related to the integration of LLMs with nursing work systems. N16 stated, \"First of all, I would prefer to know the application areas of LLMs, as well as their advantages and disadvantages.\" N15 mentioned, \"I think the search structure [of LLMs] involves certain technical aspects. I have also taken a few training sessions, and it turns out there are specific methods to use it—it’s not just about entering random information to get the desired results.\" N10 pointed out, \"If this tool is integrated into the work system, corresponding training on its usage will be necessary.\" 2.4 Theme 4: Concerns and Barriers 2.4.1 Technical Limitations of Large Language Models When applied to clinical nursing work, LLMs have inherent technical limitations. For instance, their output may contain inaccuracies or fabrications—a phenomenon commonly referred to as \"hallucinations\" in AI research. Additionally, when generating nursing-related texts, LLMs often produce content that is homogeneous and lacks innovation. In dynamically changing clinical environments, LLMs may perform poorly, failing to make timely and flexible adjustments. Furthermore, LLMs are notably inadequate in tasks requiring humanistic care, such as communicating with patients or providing psychological nursing support. N15 pointed out, \"I believe the primary challenge lies in its accuracy. Moreover, current LLMs cannot connect to databases like PubMed.\" N3 mentioned, \"For example, if I search using a specific keyword, others will undoubtedly use the same keyword too. As a result, the content it provides to me will be similar or even identical to what it offers others. While this cannot be classified as plagiarism, it does pose certain risks.\" N16 stated, \"I think LLMs can only handle fixed, mechanical tasks; they are still incapable of undertaking work that requires dynamic adjustments. For instance, nursing plans are developed based on a patient’s specific condition, but LLMs can only tell you which nursing interventions correspond to 'elevated body temperature'—they cannot formulate a comprehensive nursing plan.\" N14 emphasized, \"I believe this technology must be combined with nurses’ expertise. After all, it is just a machine, and it cannot replicate humanistic care or warmth—things like eye contact and smiles that we provide to patients.\" 2.4.2 Nurses’ Perspectives on Concerns While LLMs and related nursing robots feature low labor costs and high efficiency, some interviewees expressed concerns about potential social crises they might trigger. They argued that excessive mechanization could replace a large number of human roles, leading to social issues such as underemployment and unemployment. Furthermore, over-reliance on LLMs might cause clinical nurses to develop dependence, which would instead hinder the innovation and development of the nursing discipline. N14 pointed out, \"If these robots can indeed replace a lot of human labor, many people will lose their job opportunities as a result. There will be a large number of young people without work—could this trigger deeper problems, such as social security issues?\" N3 mentioned, \"Since these tools are very convenient to use, if nurses lack independent thinking while using them, I don’t think this will be of much help to the development of the discipline; on the contrary, it will be a constraint on innovation.\" 2.4.3 Patients’ Perspectives on Concerns The \"hallucination\" phenomenon of LLMs in the medical field is a major current safety hazard—these models may provide incorrect diagnosis and treatment recommendations, leading to misdiagnoses or suggestions of inappropriate medications. In the era of big data, the issue of privacy leakage has become increasingly severe. As the custodians and informants of patients’ private information, hospitals bear direct responsibility for protecting patient privacy; thus, ensuring the security of patient privacy is a critical prerequisite for the clinical application of LLMs. There may be significant differences in patients’ acceptance of LLMs: younger patients with higher educational backgrounds are more likely to accept AI-assisted nursing, while elderly patients—due to insufficient technical literacy or reliance on traditional medical models—pose challenges to the popularization of LLMs. N16 stated, \"I am quite concerned about its safety. LLMs are definitely unable to express themselves authentically, after all, they are not as intelligent as humans. If applied to clinical nursing, they are still quite immature.\" N9 mentioned, \"If patients’ information is recorded in this system, could it lead to the leakage of personal privacy? For instance, I have no idea when my phone number was leaked—I constantly receive calls from real estate agents and car salespeople (in frustration).\" N14 pointed out, \"It may be convenient for some people, but it is very inconvenient for the elderly. It would force them to only communicate with machines when seeking medical care, making everything even more difficult for them—they would be completely stuck.\" Discussion 3.1 Clinical Nurses Have Limited Knowledge of Large Language Models but Strong Willingness to Learn and Use Them According to the Statistical Communique on National Economic and Social Development of the People’s Republic of China in 2024 [ 13 ], by the end of 2024, the number of registered nurses in China had reached 5.84 million. Based on the requirement of a doctor-to-nurse ratio of approximately 1:2, China faces a nursing shortage of about 4.26 million. Nursing work is highly time-sensitive and practical; clinical nurses often need to simultaneously handle multiple tasks, such as patient monitoring, medical order execution, documentation, and communication with family members. This results in high work pressure, a fast-paced workflow, and frequent interruptions. Additionally, as medicine is a constantly evolving discipline, nurses frequently need to look up information such as medication dosages and administration methods, nursing operation standards, and disease-related knowledge. Regarding LLMs, nurses focus more on practical application issues rather than technical principles—such as \"Is it usable? Is it accurate? Is it safe?\" At the same time, as LLMs represent cutting-edge technology in the AI field, they involve complex professional knowledge and algorithms, posing high professional barriers and technical comprehension thresholds. This leads to the typical characteristic of \"high application demand but low technical understanding\" among clinical nurses. A cross-sectional survey conducted by Qudiamt et al. [ 14 ] among 902 healthcare professionals showed that 81.7% of respondents had not yet applied AI in medical practice, but a high proportion of 85.4% expressed willingness to further learn AI-related knowledge [ 14 ]. In the interviews of this study, all interviewees demonstrated a willingness to learn knowledge about LLMs and an intention to apply them in their work. However, some nurses remained cautious about the safety, accuracy, and ethical privacy issues of LLMs. They believed that LLMs and related nursing robots cannot fully replace clinical nurses; instead, they should serve as auxiliary tools to collaborate with nurses, thereby effectively improving clinical efficiency and patient satisfaction. A cross-sectional survey by Ozkan et al. [ 15 ] involving 115 healthcare professionals from 21 countries and regions found that 30% of the participants were concerned about the accuracy of content generated by ChatGPT, and another 12.8% had concerns about legal and ethical issues. This result is consistent with the conclusions of the aforementioned studies. 3.2 Gain In-Depth Insight into Clinical Nurses’ Application Needs and Improve Relevant Training Strategies Interview findings revealed that clinical nurses’ application needs for LLMs primarily focus on areas such as daily care, basic nursing, nursing education, and research support. By gaining in-depth understanding and conducting systematic assessments of clinical nurses’ practical needs for LLMs, and combining these with the technical characteristics of LLMs, optimizing clinical nursing workflows, improving nursing education models, and refining nursing research strategies can not only significantly enhance overall work efficiency but also effectively reduce nurses’ workload. This helps achieve the goal of \"returning time to nurses and returning nurses to patients,\" thereby improving the quality of nursing services and promoting the innovation and development of the nursing discipline in the digital era. Naureen et al. [ 16 ] conducted a descriptive cross-sectional study on AI cognition among 162 nursing students from 2 nursing colleges in Pakistan. The results showed a significant gap between this group’s technical knowledge and practical application abilities. Rony et al. [ 17 ] used qualitative research methods to survey 25 nursing students from 5 universities in Dhaka, Bangladesh. They found that nursing students were generally optimistic about the potential of AI to improve accuracy and efficiency, but at the same time, they expressed concerns about whether AI could be effectively applied in practice. They also believed that nursing curricula need to undergo comprehensive reforms, with the addition of specialized AI courses and practical training modules—this is crucial for giving full play to the potential of AI in healthcare practice. Traditional training for clinical nurses mainly focuses on nursing professional knowledge, operational skills, and relevant laws and regulations. It rarely covers knowledge related to LLMs and AI, such as basic principles, functional characteristics, application scenarios in work, and relevant legal and ethical issues. This leads to insufficient cognition of LLMs and AI among nurses, making it difficult for them to effectively apply these technologies in practical work. Interview results indicated significant differences in clinical nurses’ training needs. Therefore, based on factors such as clinical nurses’ work experience, professional titles, and specialized fields, diverse training methods that combine online and offline approaches, as well as theory and practice, should be adopted. A hierarchical and classified training system and a scientific and reasonable evaluation system should be established, and the effectiveness of training should be comprehensively assessed from multiple dimensions, including knowledge mastery, skill operation proficiency, and practical application effects. 3.3 Confront the Limitations of Artificial Intelligence, Mitigate Technical Risks, and Effectively Safeguard Patient Safety in Healthcare While LLM technology is advancing rapidly in medical and nursing fields—showcasing significant potential in applications from auxiliary diagnosis to intelligent nursing—its application value is accompanied by inherent risks and limitations. These shortcomings primarily stem from the model’s intrinsic structural features and constraints of training data [ 4 ], such as biases in information generation, privacy leakage risks, legal and ethical issues, lack of humanistic care, and absence of personalized interventions. In this study, a considerable number of interviewees expressed concerns regarding the safety, accuracy, and usability of LLMs. Additionally, strategies for LLM application should be tailored to patients with different age groups and educational backgrounds. Therefore, mitigating AI-related technical risks and effectively safeguarding patient safety in healthcare have become pressing issues that demand immediate attention. First and foremost, formulating and refining relevant laws, regulations, and usage norms is of paramount importance. Since August 2024, the European Union has officially enforced the Artificial Intelligence Act [ 18 ]—the world’s first comprehensive regulatory framework for AI. This act not only ensures the accuracy, reliability, and safety of AI tools in practice but also clarifies the scope of responsibilities for medical professionals when utilizing AI tools, thereby providing a legal basis for diagnostic and therapeutic activities. Second, as the primary users of LLMs, medical staff should establish a correct mindset and perception, clearly defining LLMs as \"assistants\" rather than \"replacements.\" While leveraging the convenient functions of LLMs to the fullest extent, nursing staff must maintain a prudent attitude toward the content generated by LLMs. This approach is essential to protect patient health and facilitate the advancement of digital and intelligent nursing practices [ 19 ]. Finally, medical professionals should gain a thorough understanding of the working principles, application scenarios, and limitations of LLMs, as well as identify the causes and manifestations of potential errors. Such knowledge enables them to more acutely detect anomalies during practical application. Meanwhile, by continuously enhancing their professional expertise and strengthening their clinical reasoning capabilities, medical staff can better integrate the advantages of this AI tool into nursing practice, ultimately delivering higher-quality and safer nursing services to patients. Conclusions This qualitative study aims to explore clinical nurses’ willingness to use Large Language Models (LLMs) and their application needs. The findings indicate that most clinical nurses hold a positive attitude toward LLMs, while a small number express doubts about the accuracy and safety of these models. Specifically, their application needs mainly focus on areas such as nursing education, clinical nursing practice, nursing system integration, and research support. Individual differences exist in clinical nurses’ willingness to learn and participate in training. Meanwhile, they also have multiple concerns regarding the clinical application of LLMs, including technical limitations, occupational risks, the possibility of over-reliance, and privacy and security issues. The focus of future work should be on optimizing the accuracy and safety of LLMs, refining their functional applications in specific clinical scenarios, and conducting targeted hierarchical training to improve clinical nurses’ acceptance and actual usage rate of LLMs. Additionally, this study has certain limitations. The scope of the research sample is relatively limited, resulting in insufficient generalizability of the conclusions, and there is a lack of adequate quantitative data support. Future studies may further expand the sample size and integrate quantitative research methods to enhance the scientific validity and reliability of the research results, thereby better promoting the popularization and application of LLMs in the field of medical care. Abbreviations Large Language Models-LLMs Artificial Intelligence-AI Declarations 1 Ethics approval and consent to participate： This study was conducted in strict accordance with the principles of the World Medical Association Declaration of Helsinki (2022 revision) for medical research involving human participants and has been approved by the Ethics committee of The Second Affiliated Hospital of Inner Mongolia Medical University (No. : EFY202500113) . This study adheres to the ethical principles of respect, beneficence, justice, and non-maleficence. Before the interview, the research team provided interviewees with a detailed explanation of the purpose and significance of the study, and required participants to sign an informed consent form. Participants had the right to withdraw from the study at any time without providing a reason. 2 Consent for publication： All participants included in this study provided written informed consent specifically authorizing the publication of their personal clinical details and any identifying images associated with the research and all the data have been released after obtaining the consent of the participants. 3 Availability of data and materials: Not applicable. This manuscript does not report data generation or analysis. 4 Competing interests: The authors declare that they have no competing interests 5 Funding: This manuscript is not supported by any funds. 6 Authors' contributions: YJG. and JTG. conceived the study, designed the qualitative research framework, and wrote the main manuscript text. SJY. led the data collection (including conducting interviews with clinical nurses) and organized the primary data repository. YW. performed the thematic analysis of interview data and drafted the results section. QWZ. provided methodological guidance on qualitative research rigor and revised the discussion section. MHR. and JFC. prepared the supplementary tables (including demographic characteristics of participants and coding framework) and verified data accuracy. YQ. secured the research funding, supervised the overall study process, and finalized the manuscript. All authors reviewed the manuscript, provided critical revisions on content and academic expression, and approved the final version for submission. 7 Acknowledgements: Not applicable. References Mitchell M, Krakauer DC. The debate over understanding in AI's large language models [J]. Proc Natl Acad Sci U S A. 2023;120(13):e2215907120. Ying JP, Dai LL, Ma JW, et al. Construction and application of an intelligent health follow - up management system for patients with chronic kidney disease [J]. J Nurs Sci. 2024;39(19):11–5. Zhou L, Wang TC, Xu M. Research progress of ChatGPT in obstetric nursing education and clinical nursing [J]. J Mod Med Health, 2024: 1–6. (In Press). Ma YZ, Wang J, Liu T, et al. A scoping review on the application of large language models in the field of nursing [J]. J Nurs Sci. 2024;39(19):124–9. Liu P, Guo N. Application progress and enlightenment of ChatGPT in the field of nursing [J]. J Nurs Adm. 2024;31(21):39–43. Mohammad Talebi H. Analyzing the Strengths, Weaknesses, Opportunities, and Threats (SWOT) of Chatbots in Emergency Nursing: A Narrative Review of Literature [J]. Creative Nurs, 2025: 10784535251341624. (In Press). Liu S, Wright AP, McCoy AB, et al. Detecting emergencies in patient portal messages using large language models and knowledge graph - based retrieval - augmented generation [J]. J Am Med Inf Assoc. 2025;32(6):1032–9. Liu T, Yang FG, Yang L, et al. A qualitative study on the experience of master's degree nursing students using artificial intelligence - generated content tools [J]. Chin J Nurs Educ. 2024;21(09):1046–51. Ma YZ, Wang J, Li XL, et al. A qualitative study on the usage experience of large language model chatbots among nursing students [J]. J Nurs Sci. 2024;39(16):69–72. Yang L, Yang ZY, Ruan H. Introduction and reflection on the standards for qualitative research reports [J]. J Nurs Sci. 2019;34(14):105–8. Liu M. Application of Colaizzi's seven - step method in data analysis of phenomenological research [J]. J Nurs Sci. 2019;34(11):90–2. Bai XR, Zhang HY, An N, et al. Research progress of digital empowerment in nursing quality management from the perspective of value co - creation [J]. Chin J Nurs. 2025;60(03):379–84. National Bureau of Statistics of China. Statistical Communique on National Economic and Social Development of the People's Republic of China in 2024 [N]. 2025-03-01. Al - Qudimat AR, Alqudimat MR, Singh K, et al. Perception and Knowledge of Hospital Workers Toward Using Artificial Intelligence: A Descriptive Study [J]. Health Sci Rep. 2025;8(5):e70623. Ozkan E, Tekin A, Ozkan MC, et al. Global Healthcare Professionals' Perceptions of Large Language Model Use in Practice: Cross - Sectional Survey Study [J]. JMIR Med Educ. 2025;11(2):e58801. Naureen M, Siddiqui S, Nasir S, et al. Awareness of the Role of Artificial Intelligence in Healthcare among Undergraduate Nursing Students: A Descriptive Cross - Sectional Study [J]. Nurse Educ Today. 2025;149:106673. Rony MKK, Ahmad S, Das DC, et al. Nursing Students' Perspectives on Integrating Artificial Intelligence Into Clinical Practice and Training: A Qualitative Descriptive Study [J]. Health Sci Rep. 2025;8(4):e70728. Van Kolfschooten H, Van Oirschot J. The EU Artificial Intelligence Act (2024): Implications for healthcare [J]. Health Policy. 2024;149:105152. Wang SY, Yang DH, Ren YD. Application scenarios and ethical discussion of large. language models in the field of nursing [J]. J Nurs Sci. 2025;40(05):108–13. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 11 Feb, 2026 Reviewers agreed at journal 02 Feb, 2026 Reviewers invited by journal 23 Jan, 2026 Editor assigned by journal 22 Jan, 2026 Editor invited by journal 02 Jan, 2026 Submission checks completed at journal 31 Dec, 2025 First submitted to journal 31 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-8471430\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":579592173,\"identity\":\"6678e031-0441-40fa-a771-2e0e09132695\",\"order_by\":0,\"name\":\"Yingjie Guo\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Inner Mongolia Medical University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Yingjie\",\"middleName\":\"\",\"lastName\":\"Guo\",\"suffix\":\"\"},{\"id\":579592174,\"identity\":\"6612c0fc-307e-4f28-9772-e32b22a233f5\",\"order_by\":1,\"name\":\"Jiantao Guo\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1UlEQVRIiWNgGAWjYBACNvbmgw8//LDh4Zd/fIA4LXw8x5KNJXvS5CQb0hKI0yInkWMmwMN22NjgQI4BkQ6TSDBjkOBhTpzZcObjjTcMdnK6DYS08DxIe1BgwZbYz9i72XIOQ7Kx2QFCWtgTjhtI8PAkzmzm3SbNw3AgcRtBLQyJbRI8bBKJG47xPCNSC0cyG1CLgbHBGR42IrXwHGMGBnKCnOQMNmPLOQZE+EW+vf8jMCr/8/BLMD+88abCTo6gFhQgwUNk1CBrIVXHKBgFo2AUjAgAANO2PtSw78IoAAAAAElFTkSuQmCC\",\"orcid\":\"\",\"institution\":\"Second Affiliated Hospital of Inner Mongolia Medical University\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Jiantao\",\"middleName\":\"\",\"lastName\":\"Guo\",\"suffix\":\"\"},{\"id\":579592175,\"identity\":\"41eebefb-ce67-4dc0-b306-7a631c260e8a\",\"order_by\":2,\"name\":\"Yi Qiu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Second Affiliated Hospital of Inner Mongolia Medical University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Yi\",\"middleName\":\"\",\"lastName\":\"Qiu\",\"suffix\":\"\"},{\"id\":579592176,\"identity\":\"95b42a20-7b38-4a5a-a834-dce8fe38d4ef\",\"order_by\":3,\"name\":\"Shengjuan Yan\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Second Affiliated Hospital of Inner Mongolia Medical University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Shengjuan\",\"middleName\":\"\",\"lastName\":\"Yan\",\"suffix\":\"\"},{\"id\":579592177,\"identity\":\"03405ec4-a712-4084-bbcd-627b1d8075fa\",\"order_by\":4,\"name\":\"Ying Wang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Second Affiliated Hospital of Inner Mongolia Medical University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Ying\",\"middleName\":\"\",\"lastName\":\"Wang\",\"suffix\":\"\"},{\"id\":579592178,\"identity\":\"9e4d4879-dc79-4a7b-ac63-a997492a407f\",\"order_by\":5,\"name\":\"Qingwei Zhu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Second Affiliated Hospital of Inner Mongolia Medical University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Qingwei\",\"middleName\":\"\",\"lastName\":\"Zhu\",\"suffix\":\"\"},{\"id\":579592179,\"identity\":\"b648a47a-bf8b-453e-bf67-69975ff7c240\",\"order_by\":6,\"name\":\"Minhua Ren\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Inner Mongolia Medical University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Minhua\",\"middleName\":\"\",\"lastName\":\"Ren\",\"suffix\":\"\"},{\"id\":579592180,\"identity\":\"ab0b10ac-728c-4192-97cd-abfee0fbcab7\",\"order_by\":7,\"name\":\"Jingfang Cao\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Inner Mongolia Medical University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Jingfang\",\"middleName\":\"\",\"lastName\":\"Cao\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2025-12-29 09:23:32\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-8471430/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-8471430/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":101397857,\"identity\":\"d8cb7246-104e-47f6-a4bd-7d2fbd06f271\",\"added_by\":\"auto\",\"created_at\":\"2026-01-29 09:37:39\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1216709,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8471430/v1/455c3052-4b92-415f-8a9b-4967afd2c657.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"A Qualitative Study on the Willingness and Application Demands of Clinical Nurses in the use of Large Language Models\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eThe development of artificial intelligence (AI) technology in the field of nursing has spanned nearly four decades, and it has demonstrated tremendous potential in enhancing the quality of clinical nursing, advancing the development of nursing education, and supporting nursing scientific research. In recent years, with the rapid advancement of AI technology, Large Language Models (LLMs) have emerged.\\u003c/p\\u003e\\n\\u003cp\\u003eLLMs are a category of language models based on the Transformer architecture, with parameter scales ranging from billions to trillions or even higher. Relying primarily on deep learning technology, these models undergo large-scale data training and are capable of performing complex tasks such as natural language processing and human-computer interaction [1]. ChatGPT, launched by OpenAI in the United States, attracted widespread attention in the medical and nursing fields immediately after its release. Additionally, localized LLMs in China, such as ERNIE Bot, DeepSeek, and Doubao, have gradually come to the fore.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eLLMs are now at the forefront of medical AI with immense potential to improve the efficiency and effectiveness of clinical, educational and research work, but they require extensive validation and further development to overcome technological weaknesses.\\u0026nbsp;China's \\\"14th Five-Year Plan\\\" clearly identifies the integration of AI technology and the field of life and health as strategic priorities. The Chinese government will provide full support in terms of funding, projects, talents and other aspects.\\u003c/p\\u003e\\n\\u003cp\\u003eCurrently, research on LLMs in the medical field is relatively extensive, but it mostly focuses on literature reviews and current situation surveys [2-7]. In China, there are only two qualitative studies on nursing students' experience of using AI tools [8, 9], while qualitative research targeting clinical nurses remains insufficient. Therefore, it is particularly important to understand clinical nurses' willingness to apply LLMs, their current needs, and their usage experience.\\u003c/p\\u003e\\n\\u003cp\\u003eThis study employs qualitative interviews to conduct in-depth analysis and interpretation of clinical nurses' willingness to use LLMs, application needs, learning and training needs, as well as concerns and worries. The aim is to provide a basis for nursing managers to develop training programs, promote the popularization of LLMs in the nursing field, and thereby improve the efficiency of clinical nursing work.\\u003c/p\\u003e\"},{\"header\":\"Participants and Methods\",\"content\":\"\\u003cdiv id=\\\"Sec2\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e1.1 Study Participants\\u003c/h2\\u003e \\u003cp\\u003e From June to August 2025, this study selected clinical nurses from multiple Grade A tertiary hospitals in Hohhot, Inner Mongolia Autonomous Region as research participants, and conducted offline semi-structured interviews. The maximum variation purposive sampling method was adopted to ensure the representativeness and typicality of the interview samples, thereby obtaining as rich data as possible.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eInclusion Criteria\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eIn-service nurses holding a nurse practitioner qualification certificate;\\u003c/p\\u003e \\u003cp\\u003eNurses who are on duty and willing to participate in this study;\\u003c/p\\u003e \\u003cp\\u003eParticipants who have been informed of the study details and consented voluntarily.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eExclusion Criteria\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eNurses who are in probation, internship, or receiving standardized training for further study.\\u003c/p\\u003e \\u003cp\\u003eThis study followed the principle of information saturation, which means that data collection was stopped when the collected information was duplicated and no new themes emerged from data analysis.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e1.2 Research Methods\\u003c/h2\\u003e \\u003cdiv id=\\\"Sec4\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e1.2.1 Development of the Interview Outline\\u003c/h2\\u003e \\u003cp\\u003eThis study was conducted in accordance with the Standards for Reporting Qualitative Research (SRQR) checklist [\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e]. To develop the interview outline, the research team first conducted a systematic literature review centered on the study objectives, and initially drafted the outline by integrating the outcomes of group discussions. To verify the practicality and validity of the draft outline, a pilot interview was conducted with 2 clinical nurses. Based on the feedback from the pilot interview and suggestions from the interviewees, the draft was revised and refined to form the final formal interview outline, which included the following questions:\\u003c/p\\u003e \\u003cp\\u003e(1) Have you heard of or do you have any understanding of Large Language Models (LLMs)? What is your first impression of LLMs?\\u003c/p\\u003e \\u003cp\\u003e(2) In your opinion, what potential application values do LLMs have in the medical field?\\u003c/p\\u003e \\u003cp\\u003e(3) Are you willing to use LLMs in your work? Please explain the reasons.\\u003c/p\\u003e \\u003cp\\u003e(4) What factors do you think will affect your willingness to use LLMs?\\u003c/p\\u003e \\u003cp\\u003e(5) Which specific nursing tasks do you think LLMs can help you accomplish?\\u003c/p\\u003e \\u003cp\\u003e(6) In your view, what advantages might the application of LLMs in nursing work bring, or what challenges might it face?\\u003c/p\\u003e \\u003cp\\u003e(7) Do you hope the hospital will offer relevant training on LLMs? Please explain the reasons.\\u003c/p\\u003e \\u003cp\\u003e(8) What suggestions or expectations do you have for the future development of LLMs in the nursing field?\\u003c/p\\u003e \\u003cp\\u003e(9) Is there any other information you would like to add?\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec5\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e1.2.2 Data Collection Methods\\u003c/h2\\u003e \\u003cp\\u003eAll members of the research team received systematic specialized training in qualitative research methodology, equipping them with a solid theoretical foundation and rich interview experience. This study adopted face-to-face in-depth interviews, which were conducted in quiet and undisturbed demonstration classrooms or meeting rooms. On-site note-taking and audio recording were combined to ensure the completeness and accuracy of the data.\\u003c/p\\u003e \\u003cp\\u003e Before each interview, researchers provided interviewees with a detailed explanation of the interview\\u0026rsquo;s purpose, content, and duration. They also informed interviewees that the entire process would be audio-recorded, while promising that the recording content would only be used for academic research and that the principle of confidentiality would be strictly followed.\\u003c/p\\u003e \\u003cp\\u003e Interviews were conducted in strict accordance with the outline, but the order of questions and questioning strategies were flexibly adjusted based on the interviewees\\u0026rsquo; responses. Researchers listened attentively and created an open atmosphere to encourage interviewees to express themselves fully and provide timely feedback. For ambiguous expressions or content that could be further explored, researchers used repeated follow-up questions to clarify the true intentions of interviewees, and also recorded non-verbal information such as facial expressions and body movements.\\u003c/p\\u003e \\u003cp\\u003eThe duration of a single interview was controlled between 20 and 30 minutes. When no new themes emerged, the interview was terminated in accordance with the theory of information saturation. Within 48 hours after the interview, the audio recording was transcribed verbatim. After repeated proofreading, the transcript was returned to the interviewee for confirmation to ensure the reliability and validity of the research data.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec6\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e1.2.3 Data Analysis Methods and Quality Control\\u003c/h2\\u003e \\u003cp\\u003e Interview participants were coded using English letters, and transcribed text data were stored and organized. The Colaizzi 7-step analysis method was adopted for data analysis [\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e], with specific steps as follows:\\u003c/p\\u003e \\u003cp\\u003e(1) Carefully read the transcribed materials to form a general understanding of all statements;\\u003c/p\\u003e \\u003cp\\u003e(2) Mark and extract statements relevant to this study;\\u003c/p\\u003e \\u003cp\\u003e(3) Code the extracted statements;\\u003c/p\\u003e \\u003cp\\u003e(4) Summarize the codes, identify common concepts, and form themes;\\u003c/p\\u003e \\u003cp\\u003e(5) Describe each theme in detail;\\u003c/p\\u003e \\u003cp\\u003e(6) Determine the final themes after analyzing similar viewpoints;\\u003c/p\\u003e \\u003cp\\u003e(7) Feed back the final themes to the interview participants for verification.\\u003c/p\\u003e \\u003cp\\u003eNVivo 14 software was used to assist in data analysis. Two researchers independently analyzed and coded the interview data to form themes. Meanwhile, the research process was documented by writing detailed memos and reflective journals to avoid bias. When disputes arose, they were resolved through discussions among members of the research team, so as to enhance the scientificity and reliability of the research results.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec7\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e1.2.4 Ethical Principles\\u003c/h2\\u003e \\u003cp\\u003e This study has adhered to the Declaration of Helsinki and obtained approval from the Ethics Committee of a Grade A tertiary hospital in Inner Mongolia, and adheres to the ethical principles of respect, beneficence, justice, and non-maleficence. Before the interview, the research team provided interviewees with a detailed explanation of the purpose and significance of the study, and required participants to sign an informed consent form. Participants had the right to withdraw from the study at any time without providing a reason.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003eA total of 16 clinical nurses were interviewed in this study. The duration of each individual interview ranged from 20 to 30 minutes, with an average duration of 23 minutes. After transcription, the interview recordings amounted to a total of 60,854 Chinese characters. The general demographic information of the interviewees is detailed in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e. Through data analysis, this study summarized 4 core themes and 12 sub-themes.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eGeneral Information of Respondents (n\\u0026thinsp;=\\u0026thinsp;16)\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"8\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCode\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eAge (Years)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eDepartment\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eWorking Experience (Years)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eNurse Grade\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eEducational Background\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eProfessional Title\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003ePosition\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eN1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e44\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eRespiratory Medicine\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e23\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eN4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eBachelor's Degree\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eAssociate Chief Nurse\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eHead Nurse\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eN2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e41\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eRespiratory Medicine\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e16\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eN4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eBachelor's Degree\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eAssociate Chief Nurse\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eNone\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eN3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e36\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eCardiology Department\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e14\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eN3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eBachelor's Degree\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eNurse Practitioner\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eHead Nurse\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eN4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e45\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eCardiology Department\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e21\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eN3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eBachelor's Degree\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eAssociate Chief Nurse\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eNone\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eN5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e33\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eCardiology Department\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eN3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eBachelor's Degree\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eNurse Practitioner\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eNone\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eN6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e46\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eNeurosurgery Department\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e 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\\u003cp\\u003e15\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eN4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eBachelor's Degree\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eChief Nurse\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eNone\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eN8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e35\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eGynecology Department\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e11\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eN4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eMaster's Degree\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eAssociate Chief Nurse\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eNone\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eN9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e40\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eGynecology Department\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e19\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eN3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eBachelor's Degree\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eNurse Practitioner\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eHead Nurse\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eN10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e24\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eOrthopedics Department\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eN1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eBachelor's Degree\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eStaff Nurse\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eNone\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003e(Continued) General Information of Respondents (n\\u0026thinsp;=\\u0026thinsp;16)\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"8\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCode\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eAge (Years)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eDepartment\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eWorking Experience (Years)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eNurse Grade\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eEducational Background\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eProfessional Title\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003ePosition\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eN12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e42\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eThoracic Surgery Department\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e16\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eN4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eBachelor's Degree\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eAssociate Chief Nurse\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eHead Nurse\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eN13\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e37\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eIntensive Care Unit (ICU)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e14\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eN2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eJunior College Degree\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eNurse Practitioner\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eNone\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eN14\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e33\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eEmergency Department\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eN3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eBachelor's Degree\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eNurse Practitioner\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eNone\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eN15\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e36\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eNeonatology Department\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e11\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eN3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eMaster's Degree\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eNurse Practitioner\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eNone\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eN16\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e32\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eEmergency Department\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eN3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eBachelor's Degree\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eNurse Practitioner\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eNone\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003e(Continued) General Information of Respondents (n\\u0026thinsp;=\\u0026thinsp;16)\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"8\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCode\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eAge (Years)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eDepartment\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eWorking Experience (Years)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eNurse Grade\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eEducational Background\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eProfessional Title\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003ePosition\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eN12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e42\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eThoracic Surgery Department\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e16\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eN4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eBachelor's Degree\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eAssociate Chief Nurse\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eHead Nurse\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eN13\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e37\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eIntensive Care Unit (ICU)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e14\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eN2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eJunior College Degree\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eNurse Practitioner\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eNone\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eN14\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e33\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eEmergency Department\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eN3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eBachelor's Degree\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eNurse Practitioner\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eNone\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eN15\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e36\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eNeonatology Department\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e11\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eN3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eMaster's Degree\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eNurse Practitioner\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eNone\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eN16\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e32\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eEmergency Department\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eN3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eBachelor's Degree\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eNurse Practitioner\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eNone\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cdiv id=\\\"Sec9\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.1 Theme 1: Cognition and Acceptance Level\\u003c/h2\\u003e \\u003cdiv id=\\\"Sec10\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e2.1.1 Overall Low Level of Cognition of Large Language Models Among Clinical Nurses\\u003c/h2\\u003e \\u003cp\\u003eWith the growing popularity of LLMs, terms such as \\\"ChatGPT\\\" and \\\"DeepSeek\\\" have gradually entered public awareness. However, interview results revealed that some interviewees\\u0026rsquo; understanding of LLMs was limited to media reports or conversations with colleagues; while others reported having attempted to use LLMs in their work to assist with certain tasks, their grasp of relevant technical knowledge remained insufficient. N4 mentioned, \\\"I have heard of and learned a bit about LLMs, but I haven\\u0026rsquo;t studied them in depth\\u0026mdash;only understanding superficial content.\\\" N13 noted, \\\"Nowadays, people have heard of LLMs, but I think very few actually apply them, especially in our professional circle, where access is quite limited.\\\" N14 stated, \\\"I know something about DeepSeek and ChatGPT, but it was only through your introduction just now that I learned these are collectively called Large Language Models.\\\"\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec11\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e2.1.2 Most Clinical Nurses Hold a Positive Attitude Toward Large Language Models\\u003c/h2\\u003e \\u003cp\\u003eOverall, interviewees expressed a positive attitude toward their experience with LLMs, and they generally described their first impression of LLMs as \\\"easy to use,\\\" \\\"convenient,\\\" and \\\"amazing.\\\" N1 stated, \\\"I have heard of this technology. Before I learned much about it, I wondered if it was really that good. But after trying it, I found it is indeed good\\u0026mdash;quite amazing.\\\" N14 mentioned, \\\"I think it is both easy to use and convenient. Especially after DeepSeek was launched, I just tried it recently. Whether I was searching for travel guides when going on a trip or writing speech drafts, it gave me a good experience.\\\"\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec12\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e2.1.3 A Minority of Clinical Nurses Hold a Skeptical Attitude Toward Large Language Models\\u003c/h2\\u003e \\u003cp\\u003eA small number of interviewees still maintained a skeptical attitude toward LLMs. They argued that the content generated by LLMs had not met their expectations, and meanwhile expressed concerns about the accuracy and safety of LLMs. N2 pointed out, \\\"In fact, LLMs can generate a lot of sophisticated or policy-oriented content, but there is actually a certain gap when it comes to practical application in my work with patients.\\\" N11 stated, \\\"I feel it may not be accurate or safe enough. When it comes to issues involving patients and human lives, I believe we need to rely on human professional judgment\\u0026mdash;I don\\u0026rsquo;t trust LLMs 100%.\\\"\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.2 Theme 2: Application Scenario Requirements\\u003c/h2\\u003e \\u003cdiv id=\\\"Sec14\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e2.2.1 Nursing Education\\u003c/h2\\u003e \\u003cp\\u003eIn the field of nursing education, interviewees pointed out that LLMs can not only assist clinical nurses in creating courseware and PPTs, but also leverage their rich data resources and human-like reasoning capabilities to enrich clinical nurses\\u0026rsquo; professional knowledge, enhance clinical skills, and improve communication abilities. N13 stated, \\\"Since our hospital is a teaching hospital, LLMs are very practical in teaching\\u0026mdash;they can help create PPTs for students.\\\" N16 proposed, \\\"For example, after I just participated in the rescue of a patient with a respiratory disease, I hope LLMs can generate all knowledge related to respiratory rescue, which will help expand our knowledge scope better.\\\" N8 indicated, \\\"In terms of nursing education, applications like virtual patients are quite effective for new nurses and nursing students.\\\"\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec15\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e2.2.2 Daily Care and Mechanical Tasks\\u003c/h2\\u003e \\u003cp\\u003eDaily care and mechanical, repetitive nursing operations occupy a large amount of clinical nurses\\u0026rsquo; working time, leading to a common feeling among nurses that they are \\\"busy with trivial tasks and unable to focus on professional work.\\\" Most interviewees expressed the expectation that LLMs could be integrated with nursing robots in the future to take on tasks related to daily care and mechanical work.\\u003c/p\\u003e \\u003cp\\u003eN4 pointed out, \\\"For example, ward rounds, measuring vital signs, replacing intravenous fluids, changing bed sheets and quilt covers, and helping patients turn over and pat their backs\\u0026mdash;these tasks actually consume a lot of human resources.\\\" N12 mentioned, \\\"For some mechanical tasks that do not require humanistic care, such as reminding patients of the time for imaging examinations or blood drawing schedules, we can use LLMs to preset the time and send regular reminders to them.\\\" N13 stated, \\\"Since tasks like medication retrieval and drug collection follow fixed procedures, LLMs can be programmed manually to perform these tasks; it can completely replace humans for fixed, mechanical work.\\\"\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec16\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e2.2.3 Nursing Information Collection and System Integration\\u003c/h2\\u003e \\u003cp\\u003eTraditional manual entry of nursing information is not only time-consuming but also prone to errors. The application of LLMs in nursing information collection and system integration not only improves clinical efficiency and accuracy but also further promotes nursing to return to its \\\"patient-centered\\\" essence [\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e]. N3 stated, \\\"If the system (an LLM-integrated nursing system) detects an increase in a patient\\u0026rsquo;s body temperature, it will pop up an abnormal alert and provide relevant nursing interventions\\u0026mdash;I find this quite convenient.\\\" N15 pointed out, \\\"In our neonatology department, there are actually many trivial nursing tasks. If there is an intelligent system to monitor completed and pending tasks, I believe the quality of clinical nursing will be higher; this is much more effective than relying on nurses\\u0026rsquo; memory to keep track of these tasks every day.\\\"\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec17\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e2.2.4 Nursing Research\\u003c/h2\\u003e \\u003cp\\u003eClinical nurses usually face the dual work pressure of \\\"research-clinical practice\\\". With multi-task processing capabilities such as natural language understanding, text generation, and data analysis, LLMs enable clinical nurses to access the latest research progress and organize analytical ideas through low-cost LLM tools, thereby providing assistance in research design. N3 pointed out, \\\"As a data storage tool, LLMs are actually far more powerful than the human brain. They contain a vast amount of information and materials, which we can retrieve easily.\\\" N13 proposed, \\\"Sometimes I have ideas in my mind but cannot summarize them accurately in words. By using ChatGPT, I can express the desired results precisely and comprehensively, which helps me organize my thoughts better.\\\" N14 stated, \\\"I don\\u0026rsquo;t have a good understanding of research content, and at the same time, I\\u0026rsquo;m engaged in clinical work. It seems I don\\u0026rsquo;t have enough time for research either. So I think if LLMs can be integrated with research\\u0026mdash;teaching us how to identify research questions in clinical work and write research papers\\u0026mdash;it would be very helpful.\\\"\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec18\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.3 Theme 3: Willingness and Needs for Learning and Training\\u003c/h2\\u003e \\u003cdiv id=\\\"Sec19\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e2.3.1 Differences in Learning Willingness\\u003c/h2\\u003e \\u003cp\\u003eMost interviewees held an open attitude toward emerging technologies and demonstrated a strong willingness to participate in training. However, a small number of interviewees maintained a negative stance toward training: they were more focused on content that could directly address practical work problems, considered training on LLMs unnecessary in personal usage scenarios, and worried that training would take up rest time, thereby further increasing their workload. N1 stated, \\\"We are still exploring ways to apply LLMs more extensively in clinical work to leverage their functions. However, the specific methods of application and the extent to which they can be utilized require further learning and exploration on our part.\\\" N15 mentioned, \\\"I would prefer to receive systematic training. I believe that only through systematic learning can we make good use of a new tool; otherwise, we can only gain a superficial understanding of it.\\\" N3 pointed out, \\\"I hope to learn about the functions that have already been integrated into the hospital\\u0026rsquo;s current system, as well as potential risks associated with them. But for tools like ChatGPT, specialized training may not be necessary, since their use depends on personal preferences or habits.\\\" N8 indicated, \\\"If the hospital plans to introduce this tool, one or two training sessions would be sufficient. If it does not plan to introduce it, there is no need for training\\u0026mdash;those who need it can learn independently. For us, additional training really becomes a burden (with a bitter laugh).\\\"\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec20\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e2.3.2 Differences in Training Needs\\u003c/h2\\u003e \\u003cp\\u003eClinical nurses exhibit differences in age, educational background, and computer literacy, which result in varied training needs for LLMs. Interview findings revealed that interviewees\\u0026rsquo; training needs primarily focused on basic concepts, usage methods, and operational skills related to the integration of LLMs with nursing work systems.\\u003c/p\\u003e \\u003cp\\u003eN16 stated, \\\"First of all, I would prefer to know the application areas of LLMs, as well as their advantages and disadvantages.\\\" N15 mentioned, \\\"I think the search structure [of LLMs] involves certain technical aspects. I have also taken a few training sessions, and it turns out there are specific methods to use it\\u0026mdash;it\\u0026rsquo;s not just about entering random information to get the desired results.\\\" N10 pointed out, \\\"If this tool is integrated into the work system, corresponding training on its usage will be necessary.\\\"\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec21\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.4 Theme 4: Concerns and Barriers\\u003c/h2\\u003e \\u003cdiv id=\\\"Sec22\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e2.4.1 Technical Limitations of Large Language Models\\u003c/h2\\u003e \\u003cp\\u003eWhen applied to clinical nursing work, LLMs have inherent technical limitations. For instance, their output may contain inaccuracies or fabrications\\u0026mdash;a phenomenon commonly referred to as \\\"hallucinations\\\" in AI research. Additionally, when generating nursing-related texts, LLMs often produce content that is homogeneous and lacks innovation. In dynamically changing clinical environments, LLMs may perform poorly, failing to make timely and flexible adjustments. Furthermore, LLMs are notably inadequate in tasks requiring humanistic care, such as communicating with patients or providing psychological nursing support. N15 pointed out, \\\"I believe the primary challenge lies in its accuracy. Moreover, current LLMs cannot connect to databases like PubMed.\\\" N3 mentioned, \\\"For example, if I search using a specific keyword, others will undoubtedly use the same keyword too. As a result, the content it provides to me will be similar or even identical to what it offers others. While this cannot be classified as plagiarism, it does pose certain risks.\\\" N16 stated, \\\"I think LLMs can only handle fixed, mechanical tasks; they are still incapable of undertaking work that requires dynamic adjustments. For instance, nursing plans are developed based on a patient\\u0026rsquo;s specific condition, but LLMs can only tell you which nursing interventions correspond to 'elevated body temperature'\\u0026mdash;they cannot formulate a comprehensive nursing plan.\\\" N14 emphasized, \\\"I believe this technology must be combined with nurses\\u0026rsquo; expertise. After all, it is just a machine, and it cannot replicate humanistic care or warmth\\u0026mdash;things like eye contact and smiles that we provide to patients.\\\"\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec23\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e2.4.2 Nurses\\u0026rsquo; Perspectives on Concerns\\u003c/h2\\u003e \\u003cp\\u003eWhile LLMs and related nursing robots feature low labor costs and high efficiency, some interviewees expressed concerns about potential social crises they might trigger. They argued that excessive mechanization could replace a large number of human roles, leading to social issues such as underemployment and unemployment. Furthermore, over-reliance on LLMs might cause clinical nurses to develop dependence, which would instead hinder the innovation and development of the nursing discipline. N14 pointed out, \\\"If these robots can indeed replace a lot of human labor, many people will lose their job opportunities as a result. There will be a large number of young people without work\\u0026mdash;could this trigger deeper problems, such as social security issues?\\\" N3 mentioned, \\\"Since these tools are very convenient to use, if nurses lack independent thinking while using them, I don\\u0026rsquo;t think this will be of much help to the development of the discipline; on the contrary, it will be a constraint on innovation.\\\"\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec24\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e2.4.3 Patients\\u0026rsquo; Perspectives on Concerns\\u003c/h2\\u003e \\u003cp\\u003eThe \\\"hallucination\\\" phenomenon of LLMs in the medical field is a major current safety hazard\\u0026mdash;these models may provide incorrect diagnosis and treatment recommendations, leading to misdiagnoses or suggestions of inappropriate medications. In the era of big data, the issue of privacy leakage has become increasingly severe. As the custodians and informants of patients\\u0026rsquo; private information, hospitals bear direct responsibility for protecting patient privacy; thus, ensuring the security of patient privacy is a critical prerequisite for the clinical application of LLMs.\\u003c/p\\u003e \\u003cp\\u003eThere may be significant differences in patients\\u0026rsquo; acceptance of LLMs: younger patients with higher educational backgrounds are more likely to accept AI-assisted nursing, while elderly patients\\u0026mdash;due to insufficient technical literacy or reliance on traditional medical models\\u0026mdash;pose challenges to the popularization of LLMs.\\u003c/p\\u003e \\u003cp\\u003eN16 stated, \\\"I am quite concerned about its safety. LLMs are definitely unable to express themselves authentically, after all, they are not as intelligent as humans. If applied to clinical nursing, they are still quite immature.\\\" N9 mentioned, \\\"If patients\\u0026rsquo; information is recorded in this system, could it lead to the leakage of personal privacy? For instance, I have no idea when my phone number was leaked\\u0026mdash;I constantly receive calls from real estate agents and car salespeople (in frustration).\\\" N14 pointed out, \\\"It may be convenient for some people, but it is very inconvenient for the elderly. It would force them to only communicate with machines when seeking medical care, making everything even more difficult for them\\u0026mdash;they would be completely stuck.\\\"\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003e3.1 Clinical Nurses Have Limited Knowledge of Large Language Models but Strong Willingness to Learn and Use Them\\u003c/p\\u003e \\u003cp\\u003eAccording to the Statistical Communique on National Economic and Social Development of the People\\u0026rsquo;s Republic of China in 2024 [\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e], by the end of 2024, the number of registered nurses in China had reached 5.84\\u0026nbsp;million. Based on the requirement of a doctor-to-nurse ratio of approximately 1:2, China faces a nursing shortage of about 4.26\\u0026nbsp;million.\\u003c/p\\u003e \\u003cp\\u003eNursing work is highly time-sensitive and practical; clinical nurses often need to simultaneously handle multiple tasks, such as patient monitoring, medical order execution, documentation, and communication with family members. This results in high work pressure, a fast-paced workflow, and frequent interruptions. Additionally, as medicine is a constantly evolving discipline, nurses frequently need to look up information such as medication dosages and administration methods, nursing operation standards, and disease-related knowledge.\\u003c/p\\u003e \\u003cp\\u003eRegarding LLMs, nurses focus more on practical application issues rather than technical principles\\u0026mdash;such as \\\"Is it usable? Is it accurate? Is it safe?\\\" At the same time, as LLMs represent cutting-edge technology in the AI field, they involve complex professional knowledge and algorithms, posing high professional barriers and technical comprehension thresholds. This leads to the typical characteristic of \\\"high application demand but low technical understanding\\\" among clinical nurses.\\u003c/p\\u003e \\u003cp\\u003eA cross-sectional survey conducted by Qudiamt et al. [\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e] among 902 healthcare professionals showed that 81.7% of respondents had not yet applied AI in medical practice, but a high proportion of 85.4% expressed willingness to further learn AI-related knowledge [\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e]. In the interviews of this study, all interviewees demonstrated a willingness to learn knowledge about LLMs and an intention to apply them in their work. However, some nurses remained cautious about the safety, accuracy, and ethical privacy issues of LLMs. They believed that LLMs and related nursing robots cannot fully replace clinical nurses; instead, they should serve as auxiliary tools to collaborate with nurses, thereby effectively improving clinical efficiency and patient satisfaction.\\u003c/p\\u003e \\u003cp\\u003eA cross-sectional survey by Ozkan et al. [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e] involving 115 healthcare professionals from 21 countries and regions found that 30% of the participants were concerned about the accuracy of content generated by ChatGPT, and another 12.8% had concerns about legal and ethical issues. This result is consistent with the conclusions of the aforementioned studies.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec26\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.2 Gain In-Depth Insight into Clinical Nurses\\u0026rsquo; Application Needs and Improve Relevant Training Strategies\\u003c/h2\\u003e \\u003cp\\u003eInterview findings revealed that clinical nurses\\u0026rsquo; application needs for LLMs primarily focus on areas such as daily care, basic nursing, nursing education, and research support. By gaining in-depth understanding and conducting systematic assessments of clinical nurses\\u0026rsquo; practical needs for LLMs, and combining these with the technical characteristics of LLMs, optimizing clinical nursing workflows, improving nursing education models, and refining nursing research strategies can not only significantly enhance overall work efficiency but also effectively reduce nurses\\u0026rsquo; workload. This helps achieve the goal of \\\"returning time to nurses and returning nurses to patients,\\\" thereby improving the quality of nursing services and promoting the innovation and development of the nursing discipline in the digital era.\\u003c/p\\u003e \\u003cp\\u003eNaureen et al. [\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e] conducted a descriptive cross-sectional study on AI cognition among 162 nursing students from 2 nursing colleges in Pakistan. The results showed a significant gap between this group\\u0026rsquo;s technical knowledge and practical application abilities. Rony et al. [\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e] used qualitative research methods to survey 25 nursing students from 5 universities in Dhaka, Bangladesh. They found that nursing students were generally optimistic about the potential of AI to improve accuracy and efficiency, but at the same time, they expressed concerns about whether AI could be effectively applied in practice. They also believed that nursing curricula need to undergo comprehensive reforms, with the addition of specialized AI courses and practical training modules\\u0026mdash;this is crucial for giving full play to the potential of AI in healthcare practice.\\u003c/p\\u003e \\u003cp\\u003eTraditional training for clinical nurses mainly focuses on nursing professional knowledge, operational skills, and relevant laws and regulations. It rarely covers knowledge related to LLMs and AI, such as basic principles, functional characteristics, application scenarios in work, and relevant legal and ethical issues. This leads to insufficient cognition of LLMs and AI among nurses, making it difficult for them to effectively apply these technologies in practical work.\\u003c/p\\u003e \\u003cp\\u003eInterview results indicated significant differences in clinical nurses\\u0026rsquo; training needs. Therefore, based on factors such as clinical nurses\\u0026rsquo; work experience, professional titles, and specialized fields, diverse training methods that combine online and offline approaches, as well as theory and practice, should be adopted. A hierarchical and classified training system and a scientific and reasonable evaluation system should be established, and the effectiveness of training should be comprehensively assessed from multiple dimensions, including knowledge mastery, skill operation proficiency, and practical application effects.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003e3.3 Confront the Limitations of Artificial Intelligence, Mitigate Technical Risks, and Effectively Safeguard Patient Safety in Healthcare\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eWhile LLM technology is advancing rapidly in medical and nursing fields\\u0026mdash;showcasing significant potential in applications from auxiliary diagnosis to intelligent nursing\\u0026mdash;its application value is accompanied by inherent risks and limitations. These shortcomings primarily stem from the model\\u0026rsquo;s intrinsic structural features and constraints of training data [\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e], such as biases in information generation, privacy leakage risks, legal and ethical issues, lack of humanistic care, and absence of personalized interventions. In this study, a considerable number of interviewees expressed concerns regarding the safety, accuracy, and usability of LLMs. Additionally, strategies for LLM application should be tailored to patients with different age groups and educational backgrounds.\\u003c/p\\u003e \\u003cp\\u003eTherefore, mitigating AI-related technical risks and effectively safeguarding patient safety in healthcare have become pressing issues that demand immediate attention. First and foremost, formulating and refining relevant laws, regulations, and usage norms is of paramount importance. Since August 2024, the European Union has officially enforced the Artificial Intelligence Act [\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e]\\u0026mdash;the world\\u0026rsquo;s first comprehensive regulatory framework for AI. This act not only ensures the accuracy, reliability, and safety of AI tools in practice but also clarifies the scope of responsibilities for medical professionals when utilizing AI tools, thereby providing a legal basis for diagnostic and therapeutic activities.\\u003c/p\\u003e \\u003cp\\u003eSecond, as the primary users of LLMs, medical staff should establish a correct mindset and perception, clearly defining LLMs as \\\"assistants\\\" rather than \\\"replacements.\\\" While leveraging the convenient functions of LLMs to the fullest extent, nursing staff must maintain a prudent attitude toward the content generated by LLMs. This approach is essential to protect patient health and facilitate the advancement of digital and intelligent nursing practices [\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eFinally, medical professionals should gain a thorough understanding of the working principles, application scenarios, and limitations of LLMs, as well as identify the causes and manifestations of potential errors. Such knowledge enables them to more acutely detect anomalies during practical application. Meanwhile, by continuously enhancing their professional expertise and strengthening their clinical reasoning capabilities, medical staff can better integrate the advantages of this AI tool into nursing practice, ultimately delivering higher-quality and safer nursing services to patients.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Conclusions\",\"content\":\"\\u003cp\\u003eThis qualitative study aims to explore clinical nurses\\u0026rsquo; willingness to use Large Language Models (LLMs) and their application needs. The findings indicate that most clinical nurses hold a positive attitude toward LLMs, while a small number express doubts about the accuracy and safety of these models. Specifically, their application needs mainly focus on areas such as nursing education, clinical nursing practice, nursing system integration, and research support. Individual differences exist in clinical nurses\\u0026rsquo; willingness to learn and participate in training. Meanwhile, they also have multiple concerns regarding the clinical application of LLMs, including technical limitations, occupational risks, the possibility of over-reliance, and privacy and security issues. The focus of future work should be on optimizing the accuracy and safety of LLMs, refining their functional applications in specific clinical scenarios, and conducting targeted hierarchical training to improve clinical nurses\\u0026rsquo; acceptance and actual usage rate of LLMs. Additionally, this study has certain limitations. The scope of the research sample is relatively limited, resulting in insufficient generalizability of the conclusions, and there is a lack of adequate quantitative data support. Future studies may further expand the sample size and integrate quantitative research methods to enhance the scientific validity and reliability of the research results, thereby better promoting the popularization and application of LLMs in the field of medical care.\\u003c/p\\u003e \"},{\"header\":\"Abbreviations\",\"content\":\"\\u003cp\\u003eLarge Language Models-LLMs\\u003c/p\\u003e\\n\\u003cp\\u003eArtificial Intelligence-AI\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e1 Ethics approval and consent to participate：\\u003c/p\\u003e\\n\\u003cp\\u003eThis study was conducted in strict accordance with the principles of the \\u003cstrong\\u003eWorld Medical Association Declaration of Helsinki\\u003c/strong\\u003e (2022 revision) for medical research involving human participants and has been approved by the\\u003cstrong\\u003e\\u0026nbsp;Ethics committee of The Second Affiliated Hospital of Inner Mongolia Medical University (No. : EFY202500113)\\u003c/strong\\u003e. \\u0026nbsp;This study adheres to the ethical principles of respect, beneficence, justice, and non-maleficence. Before the interview, the research team provided interviewees with a detailed explanation of the purpose and significance of the study, and required participants to sign an informed consent form. Participants had the right to withdraw from the study at any time without providing a reason.\\u003c/p\\u003e\\n\\u003cp\\u003e2 Consent for publication：\\u003c/p\\u003e\\n\\u003cp\\u003eAll participants included in this study provided written informed consent specifically authorizing the publication of their personal clinical details and any identifying images associated with the research and all the data have been released after obtaining the consent of the participants.\\u003c/p\\u003e\\n\\u003cp\\u003e3 Availability of data and materials:\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable. This manuscript does not report data generation or analysis.\\u003c/p\\u003e\\n\\u003cp\\u003e4 Competing interests:\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare that they have no competing interests\\u003c/p\\u003e\\n\\u003cp\\u003e5 Funding:\\u003c/p\\u003e\\n\\u003cp\\u003eThis manuscript is not supported by any funds.\\u003c/p\\u003e\\n\\u003cp\\u003e6 Authors' contributions:\\u003c/p\\u003e\\n\\u003cp\\u003eYJG. and JTG. conceived the study, designed the qualitative research framework, and wrote the main manuscript text. SJY. led the data collection (including conducting interviews with clinical nurses) and organized the primary data repository. YW. performed the thematic analysis of interview data and drafted the results section. QWZ. provided methodological guidance on qualitative research rigor and revised the discussion section. MHR. and JFC. prepared the supplementary tables (including demographic characteristics of participants and coding framework) and verified data accuracy. YQ. secured the research funding, supervised the overall study process, and finalized the manuscript. All authors reviewed the manuscript, provided critical revisions on content and academic expression, and approved the final version for submission.\\u003c/p\\u003e\\n\\u003cp\\u003e7 Acknowledgements:\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eMitchell M, Krakauer DC. The debate over understanding in AI's large language models [J]. Proc Natl Acad Sci U S A. 2023;120(13):e2215907120.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eYing JP, Dai LL, Ma JW, et al. Construction and application of an intelligent health follow - up management system for patients with chronic kidney disease [J]. J Nurs Sci. 2024;39(19):11\\u0026ndash;5.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eZhou L, Wang TC, Xu M. Research progress of ChatGPT in obstetric nursing education and clinical nursing [J]. J Mod Med Health, 2024: 1\\u0026ndash;6. (In Press).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMa YZ, Wang J, Liu T, et al. A scoping review on the application of large language models in the field of nursing [J]. J Nurs Sci. 2024;39(19):124\\u0026ndash;9.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLiu P, Guo N. Application progress and enlightenment of ChatGPT in the field of nursing [J]. J Nurs Adm. 2024;31(21):39\\u0026ndash;43.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMohammad Talebi H. Analyzing the Strengths, Weaknesses, Opportunities, and Threats (SWOT) of Chatbots in Emergency Nursing: A Narrative Review of Literature [J]. Creative Nurs, 2025: 10784535251341624. (In Press).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLiu S, Wright AP, McCoy AB, et al. Detecting emergencies in patient portal messages using large language models and knowledge graph - based retrieval - augmented generation [J]. J Am Med Inf Assoc. 2025;32(6):1032\\u0026ndash;9.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLiu T, Yang FG, Yang L, et al. A qualitative study on the experience of master's degree nursing students using artificial intelligence - generated content tools [J]. Chin J Nurs Educ. 2024;21(09):1046\\u0026ndash;51.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMa YZ, Wang J, Li XL, et al. A qualitative study on the usage experience of large language model chatbots among nursing students [J]. J Nurs Sci. 2024;39(16):69\\u0026ndash;72.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eYang L, Yang ZY, Ruan H. Introduction and reflection on the standards for qualitative research reports [J]. J Nurs Sci. 2019;34(14):105\\u0026ndash;8.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLiu M. Application of Colaizzi's seven - step method in data analysis of phenomenological research [J]. J Nurs Sci. 2019;34(11):90\\u0026ndash;2.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBai XR, Zhang HY, An N, et al. Research progress of digital empowerment in nursing quality management from the perspective of value co - creation [J]. Chin J Nurs. 2025;60(03):379\\u0026ndash;84.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eNational Bureau of Statistics of China. Statistical Communique on National Economic and Social Development of the People's Republic of China in 2024 [N]. 2025-03-01.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eAl - Qudimat AR, Alqudimat MR, Singh K, et al. Perception and Knowledge of Hospital Workers Toward Using Artificial Intelligence: A Descriptive Study [J]. Health Sci Rep. 2025;8(5):e70623.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eOzkan E, Tekin A, Ozkan MC, et al. Global Healthcare Professionals' Perceptions of Large Language Model Use in Practice: Cross - Sectional Survey Study [J]. JMIR Med Educ. 2025;11(2):e58801.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eNaureen M, Siddiqui S, Nasir S, et al. Awareness of the Role of Artificial Intelligence in Healthcare among Undergraduate Nursing Students: A Descriptive Cross - Sectional Study [J]. Nurse Educ Today. 2025;149:106673.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eRony MKK, Ahmad S, Das DC, et al. Nursing Students' Perspectives on Integrating Artificial Intelligence Into Clinical Practice and Training: A Qualitative Descriptive Study [J]. Health Sci Rep. 2025;8(4):e70728.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eVan Kolfschooten H, Van Oirschot J. The EU Artificial Intelligence Act (2024): Implications for healthcare [J]. Health Policy. 2024;149:105152.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eWang SY, Yang DH, Ren YD. Application scenarios and ethical discussion of large.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003elanguage models in the field of nursing [J]. J Nurs Sci. 2025;40(05):108\\u0026ndash;13.\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"bmc-nursing\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"nurs\",\"sideBox\":\"Learn more about [BMC Nursing](http://bmcnurs.biomedcentral.com/)\",\"snPcode\":\"\",\"submissionUrl\":\"https://www.editorialmanager.com/nurs/default.aspx\",\"title\":\"BMC Nursing\",\"twitterHandle\":\"@BMC_series\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-8471430/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-8471430/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003e\\u003cstrong\\u003eBackground:\\u003c/strong\\u003e This study aims to investigate the willingness of clinical nurses to adopt large language models (LLMs) and their specific application requirements. The findings are intended to provide a scientific basis for promoting the rational integration of LLMs into nursing practice and improving the efficiency of clinical nursing work.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eMethods:\\u003c/strong\\u003e A descriptive qualitative research design was adopted. Using the maximum variation purposive sampling method, clinical nurses from multiple Class-A tertiary hospitals were recruited as participants in Hohhot City, Inner Mongolia between June and August 2025. Semi-structured interviews were conducted offline. After transcribing the interview recordings, an in-depth data analysis was performed using Colaizzi's seven-step approach.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eResults:\\u003c/strong\\u003e A total of 16 participants were included in the study. Four main themes and twelve sub-themes emerged: Cognition and Acceptance (Clinical nurses generally have limited understanding of LLMs; most hold positive attitudes toward their potential use, while a minority express skepticism); Application Scenario Requirements (Including nursing education, routine care and repetitive tasks, nursing information collection and system integration, and nursing research); Learning and Training Willingness and Needs (Participants demonstrated varying levels of interest in learning about LLMs and differing training needs); Concerns and Barriers (Technical limitations of LLMs, concerns from the perspective of clinical nurses, and patient-related apprehensions were identified).\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConclusion:\\u003c/strong\\u003e Clinical nurses exhibit both interest in and reservations about the application of LLMs. Future efforts should focus on enhancing the technical safety and reliability of LLMs, developing functional modules tailored to nursing practice, and implementing targeted, stratified training programs. These measures will increase the acceptance and utilization of LLMs among clinical nurses, ultimately contributing to improved nursing efficiency and quality.\\u003c/p\\u003e\",\"manuscriptTitle\":\"A Qualitative Study on the Willingness and Application Demands of Clinical Nurses in the use of Large Language Models\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2026-01-27 18:34:43\",\"doi\":\"10.21203/rs.3.rs-8471430/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-02-11T20:28:43+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"176941075823668917041889051346710000456\",\"date\":\"2026-02-02T16:01:07+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2026-01-23T06:12:53+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2026-01-22T09:59:09+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvited\",\"content\":\"\",\"date\":\"2026-01-02T12:27:27+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2025-12-31T15:53:05+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"BMC Nursing\",\"date\":\"2025-12-31T15:44:32+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"bmc-nursing\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"nurs\",\"sideBox\":\"Learn more about [BMC Nursing](http://bmcnurs.biomedcentral.com/)\",\"snPcode\":\"\",\"submissionUrl\":\"https://www.editorialmanager.com/nurs/default.aspx\",\"title\":\"BMC Nursing\",\"twitterHandle\":\"@BMC_series\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"a48fe168-8d73-4e74-8287-6ad826f485e6\",\"owner\":[],\"postedDate\":\"January 27th, 2026\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"under-review\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-01-27T18:34:43+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2026-01-27 18:34:43\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-8471430\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-8471430\",\"identity\":\"rs-8471430\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}