Emotional analysis of operating room nurses in acute care hospitals in Japan: insights using ChatGPT

This study seeks to address several key research questions. First, it aims to elucidate the emotions of operating room nurses in Japan towards perioperative nursing by integrating traditional qualitative analysis and generative AI. Second, it evaluates the role of generative AI, particularly ChatGPT, as a supplementary tool in emotional analysis, focusing on its practical usability and limitations. Third, it explores how themes such as patient care, surgical safety, and nursing skills interrelate within the emotional experiences of these nurses. Fourth, it aims to identify the factors contributing to burnout and turnover in the perioperative setting and examines strategies to mitigate these challenges. Finally, it investigates the impact of effective communication on the emotional challenges faced by operating room nurses, emphasizing its role in supporting their well-being and job satisfaction.

The objective of this study was to elucidate the emotions of operating room nurses in Japan towards perioperative nursing by integrating traditional qualitative analysis with generative AI. This approach aimed to explore the potential of generative AI, specifically ChatGPT, as a supplementary tool for identifying emotional states and addressing factors that contribute to burnout and turnover among operating room nurses. This study employed a single-center cross-sectional design and followed a qualitative research approach. Semi-structured interviews were conducted to gather in-depth data on the emotional experiences of operating room nurses. The study was designed and reported in accordance with the Consolidated Criteria for Reporting Qualitative Research (COREQ) [13]. Ethical approval was obtained from the relevant institutional review board before commencing the study.

This study was approved by the Ethics Committee of Medical Center (Approval No. 2023058). This study was conducted in accordance with the ethical standards of the Declaration of Helsinki (1964) and its amendments. Informed consent was obtained from all participants prior to their inclusion in the study. The participants were approached face-to-face by the researcher. Participants were provided with detailed explanations of the study’s purpose, methods, risks, and benefits, ensuring their voluntary participation. Privacy and confidentiality of the participants were strictly protected. The recorded data and interview content were anonymized and handled with the utmost care to ensure data security.

The study was conducted at Medical Center, one of the national hospitals in Japan. The duration of this study was from February 2023 to February 2024. This facility was chosen due to its comprehensive perioperative care services and the presence of a well-established Clinical Ladder program for operating room nurses. The study setting provided an appropriate environment to explore the emotions and experiences of operating room nurses in a real-world clinical context. The study population consisted of operating room nurses employed at Medical Center. Participants were selected based on specific criteria: the inclusion criteria required participants to be operating room nurses with Clinical Ladder Level II or higher, with no restrictions on gender or age. Nurses with Clinical Ladder Level I were excluded from the study. Level 1 refers to new nurses who can practice nursing according to basic nursing methods while obtaining advice when necessary, whereas Level 2 refers to nurses who can independently practice nursing based on standard nursing plans. A total of 10 participants were included in the study. These nurses represented a range of experiences and backgrounds, providing a comprehensive view of the emotional landscape in the operating room setting. The participants were aware that the researcher was a professional in perioperative nursing and had conducted extensive research in the field of perioperative nursing.

The primary outcome of this study was to identify and analyze the emotions of operating room nurses towards perioperative nursing through a combined approach of traditional qualitative analysis and generative AI. Data collection was conducted using semi-structured interviews to obtain in-depth insights into the nurses’ emotional experiences and perceptions of their work environment. The interviews were scheduled during weekday daytime hours to fit within the nurses’ regular work schedules. The data was collected at the participants’ workplace. An IC recorder was used to capture the interviews, ensuring accurate and comprehensive data collection. The interviews were conducted by the chief researcher, a male, who possesses excellent interviewing skills and has conducted interviews in numerous previous studies. The chief researcher and the study participants had an established a relationship prior to the commencement of the study through their shared experiences in operating room nursing. No one else was present besides the participants and the chief researcher. The interview guide included open-ended questions designed to elicit detailed responses about the nurses’ emotional experiences, coping strategies, and perceptions of their work environment. The interview guide used in this study was specifically developed for this research. An English language version of the interview guide is provided as a supplementary file (Supplementary File 1). The interview questions and prompts were provided by the authors to the participants. The interview guide was pilot tested multiple times. The interviews covered topics such as stress factors, emotional challenges, sources of job satisfaction, and the impact of interprofessional relationships on their emotional well-being. Following data collection, the interviews were transcribed verbatim to ensure the accuracy of the data. The transcriptions were then reviewed for significant statements and phrases related to the emotional experiences and challenges faced by the operating room nurses. These significant statements were coded and grouped into broader themes for further analysis. The primary outcome focused on identifying key themes and patterns in the emotions expressed by the nurses, with an emphasis on understanding the factors contributing to their emotional well-being and job satisfaction.

The data analysis for this study was comprehensive and multifaceted, designed to thoroughly explore the emotions of operating room nurses. The analysis proceeded through multiple stages, integrating both qualitative and supplementary quantitative methods to ensure a robust understanding of the data. Traditional qualitative analysis served as the primary approach, while generative AI (ChatGPT-4o, OpenAI, San Francisco, CA) was utilized as a supplementary tool to identify patterns and enhance efficiency in processing large volumes of text.

Thematic Analysis: The initial phase of data analysis involved thematic analysis of the transcribed semi-structured interviews. Each interview was meticulously transcribed verbatim to ensure the accuracy of the data. The transcriptions were then reviewed to identify significant statements and phrases that related to the emotional experiences and challenges faced by the operating room nurses. These significant statements were coded and grouped into broader themes using ChatGPT-4o. The AI tool assisted in the coding process by identifying patterns, relationships, and recurring themes within the transcribed data. This included recognizing emotional tones and categorizing the data accordingly. Key themes that emerged included stress factors, emotional challenges, job satisfaction, coping strategies, and the impact of interprofessional relationships on the nurses’ emotional well-being. To ensure the reliability and validity of the thematic analysis, multiple researchers reviewed the coded data and themes. Discrepancies were resolved through consensus, and the final themes were agreed upon by the entire research team.

Emotional and Subjectivity Analysis: Following the thematic analysis, Emotional analysis was performed using generative AI to quantify the emotional tone of the interview data. This involved assessing whether the statements made by the nurses were positive, negative, or neutral. Emotional scores were calculated for each interview, providing a quantitative measure of the emotional tone. Each statement was assigned an emotional score on a scale from − 100 to + 100. A score close to + 100 indicated a highly positive emotion, while a score close to -100 indicated a highly negative emotion. Scores around 0 indicated a neutral emotion. Alongside Emotional analysis, subjectivity analysis was conducted to determine the extent to which the statements were based on personal opinions and feelings versus factual information. Subjectivity scores were also computed for each interview. The subjectivity analysis was conducted to determine the extent to which the statements were based on personal opinions and feelings versus factual information. Each statement was assigned a subjectivity score on a scale from − 100 to 100. A score close to 100 indicated a highly subjective statement, reflecting personal opinions and feelings, whereas a score close to -100 indicated a highly objective statement, based more on factual information. This study utilized ChatGPT-4o as a supplementary tool to assist in the emotional and subjectivity analysis of the interview data. While ChatGPT provided a systematic and efficient way to process large amounts of text, it was not intended to replace human interpretation. Instead, it served as a complementary method to provide initial insights into emotional patterns, which were further analyzed and contextualized by human researchers. The aim was to explore how AI could support traditional qualitative methods, rather than validate ChatGPT as a stand-alone analytical tool for emotional analysis.

To enhance the reliability of the analysis, two independent human researchers with expertise in qualitative research conducted manual emotional analyses of the same interview data. Working independently of AI tools, these researchers identified key emotional themes, assigned emotional and subjectivity scores, and interpreted the emotional tones expressed by the nurses. Any discrepancies between the researchers’ findings were resolved through thorough discussion, leading to a consensus on the emotional content of the interviews. By integrating human analysis with ChatGPT’s insights, this dual approach ensured a comprehensive understanding of the data, capturing both broad emotional patterns and nuanced details.

Keyword Co-occurrence Network: To visualize the relationships between frequently mentioned terms in the interviews, a keyword co-occurrence network was created. This network depicted how different keywords were interconnected, helping to identify common themes and the relationships between various concepts discussed by the nurses.

Cluster Analysis: Cluster analysis was performed on the Emotional and subjectivity scores to identify distinct groups of emotional experiences among the nurses. This statistical method grouped the data into clusters based on similarities in Emotional and subjectivity scores. The resulting clusters were analyzed to identify common characteristics and differences, providing deeper insights into the emotional dynamics and varied experiences of the nurses.

Statistical Analysis: Descriptive statistics were used to summarize the distributions of Emotional and subjectivity scores. Various visualizations, including scatter plots and histograms, were generated to illustrate the relationships between Emotional scores, subjectivity scores, and other relevant variables such as text length. These visual tools helped to provide a clearer understanding of the data and highlighted key patterns and trends.

The results of these analyses were visualized through several types of charts and graphs. The keyword co-occurrence network illustrated the relationships between frequently mentioned keywords, providing insights into common themes and connections between various concepts. The cluster analysis on Emotional and subjectivity scores was visualized to show how the data grouped into distinct clusters, revealing patterns of emotional experiences among the nurses. The distribution of Emotional scores was displayed using histograms, highlighting the frequency and range of emotional tones expressed by the participants. Scatter plots were used to depict the relationships between Emotional scores and subjectivity scores, as well as the relationship between text length and Emotional scores. These visualizations collectively offered a comprehensive view of the emotional states of operating room nurses, aiding in the identification of areas for potential intervention to enhance their well-being and job satisfaction.

For the English proofreading of this manuscript, we utilized ChatGPT-4o, a large language model developed by OpenAI. ChatGPT-4o contributed to the improvement of grammar, vocabulary, style, and structure, thereby enhancing the quality of the manuscript. Specifically, it provided suggestions to improve the clarity and consistency of the text. Care was taken to ensure that the model’s suggestions accurately reflected the researchers’ intentions and that technical terms and specialized content were appropriately conveyed. All suggestions and modifications generated by ChatGPT-4o were reviewed and approved by the researchers before being incorporated into the final manuscript.

The study included 10 operating room nurses from Medical Center. No participants refused to participate or dropped out of the study. Their demographics and professional backgrounds are summarized in Table 1. These participants represented a range of experiences and backgrounds, providing a comprehensive view of the emotional landscape in the operating room setting. All participants were full-time employees, with varying years of nursing experience and operating room experience. Some participants also had experience outside the operating room and held licenses other than nursing. The highest educational qualifications of the participants ranged from vocational school to master’s degrees. Only one interview was conducted with each participant. The total interview time was 200 min, with an average of 20 min per interview.

The interview data was categorized into several key themes, which are summarized in Table 2 along with their corresponding Emotional and subjectivity scores.

Patient Care emerged as a dominant theme, with nurses frequently discussing their interactions with patients, the emotional challenges they face, and their efforts to ensure patient safety and comfort. Statements in this category reflected a range of emotions, from the stress of dealing with anxious patients to the satisfaction of successfully advocating for patient needs. The Emotional scores for patient care varied widely, indicating both positive and negative emotional tones. Subjectivity scores in this category were generally high, highlighting the personal and empathetic nature of the nurses’ reflections.

Surgical Safety was another significant theme. Nurses emphasized the importance of maintaining a safe surgical environment, both for patients and for themselves. They described meticulous routines for checking equipment and procedures, and the critical need for effective communication among surgical team members. The Emotional scores in this category were generally positive, reflecting a strong commitment to safety and the professional fulfillment derived from ensuring patient well-being. Subjectivity scores were also high, underscoring the personal investment nurses have in their roles.

Nursing Skills involved discussions about the technical and practical aspects of the nurses’ daily routines. Statements here included the use of skills, tools, and materials essential for surgical procedures. The Emotional scores were relatively neutral, suggesting that these tasks are viewed as routine but crucial components of their job. The subjectivity scores were lower compared to other categories, indicating a more objective focus on technical proficiency.

Several uncategorized statements revealed additional insights into the nurses’ professional lives and personal reflections. These included comments on the challenges of maintaining professional standards, the need for continuous learning, and the emotional toll of dealing with life-and-death situations. The Emotional scores for these statements varied, reflecting a mix of frustration, determination, and pride. Subjectivity scores were generally high, indicating the deeply personal nature of these reflections.

To evaluate the practical usability of ChatGPT in emotional analysis, we compared its results with those generated by two independent human researchers. The human researchers manually coded and analyzed the same interview data used in the AI analysis, focusing on identifying key emotional themes, assigning emotional and subjectivity scores, and interpreting the emotional tone expressed by the participants.

Both human researchers and ChatGPT-4o identified core emotional themes, including patient care, surgical safety, and job satisfaction. Patient care emerged as the most dominant theme in both analyses, frequently referencing stress, empathy, and emotional exhaustion. However, the human researchers emphasized subtle themes such as frustration with work conditions and anxiety about team communication, which ChatGPT did not highlight as strongly.

The comparison revealed differences in emotional scoring. Statements involving high-stakes surgical procedures received higher negative emotional scores from the human researchers due to their perceived intensity. In contrast, ChatGPT often assigned neutral scores to such statements, likely reflecting its lack of contextual awareness. Human researchers also identified mixed emotions—both positive and negative—within the same statements, whereas ChatGPT tended to classify emotions in binary terms (positive, neutral, or negative).

Subjectivity scores were generally consistent between the two analyses, with both identifying personal and emotional reflections in most statements. However, human researchers classified a broader range of statements as highly subjective, particularly those involving personal anecdotes or emotionally charged scenarios, such as interactions with patients’ families. ChatGPT, by contrast, assigned lower subjectivity scores to statements with implicit emotional content, focusing instead on explicit language.

Human researchers excelled in detecting emotional subtleties and context-specific nuances that ChatGPT missed. For example, when a participant described anxiety in handling an emergency case, human researchers captured the stress along with inferred emotions like personal responsibility and vulnerability. They also identified moments of emotional ambivalence, such as simultaneous pride and frustration, which ChatGPT categorized as general stress.

ChatGPT demonstrated efficiency in processing large volumes of data, quickly identifying broad emotional patterns and providing quantitative analyses of emotional tones across the dataset. This systematic approach enabled the visualization of emotional trends, which would have been more time-consuming with manual analysis. ChatGPT’s consistency in applying emotional and subjectivity scores ensured objectivity and reproducibility, reducing the variability often introduced by human interpretation.

The keyword co-occurrence network provided a visual representation of the relationships between frequently mentioned terms in the interview data, highlighting key concepts and their interconnections (Fig. 1). At the center of the network was the node “patients,” indicating that much of the discussion revolved around patient-related topics. Surrounding this central node were several significant themes. One prominent theme was “surgery” and “safety,” which were closely linked. This reflected the nurses’ emphasis on ensuring safe surgical procedures, highlighting the critical importance of safety protocols and practices in the operating room. Another key theme was “support” and “care,” underscoring the nurses’ focus on providing both emotional and practical support to patients. This theme emphasized the holistic approach to patient care, addressing both physical and emotional needs. Similarly, “recovery” and “treatment” were significant terms in the network, indicating the nurses’ involvement in the postoperative phase and their role in facilitating patient recovery and managing treatment plans. The terms “communicate” and “understanding” highlighted the importance of effective communication between nurses, patients, and other healthcare professionals. Effective communication was crucial for understanding patient needs and providing appropriate care. Additionally, the presence of the keywords “emotional” and “feelings” pointed to the emotional aspect of nursing work, where nurses dealt with their own and patients’ emotions. This reflected the psychological and empathetic dimensions of their roles. Challenges in nurse-patient interactions were suggested by the terms “interaction” and “difficult,” particularly in the context of stressful and high-stakes surgical environments. This pointed to the complexities of maintaining effective communication and emotional support under pressure. Furthermore, the keywords “needs” and “discuss” emphasized the importance of understanding and addressing patient needs through discussions and consultations, highlighting the nurses’ proactive approach in identifying and meeting patient requirements.

The cluster analysis, depicted in Fig. 2, revealed three distinct clusters based on Emotional and subjectivity scores. Cluster 1 (red) represented statements with predominantly positive Emotional and moderate to high subjectivity. These statements often highlighted positive aspects of patient care, job satisfaction, and effective communication. Cluster 2 (green) encompassed statements with lower Emotional scores and varying levels of subjectivity, indicating a mix of neutral or slightly negative Emotionals often associated with challenges and routine tasks. Cluster 3 (blue) included statements with a wider range of Emotional scores but consistently high subjectivity, reflecting personal and empathetic responses to both positive and negative experiences in the operating room.

The scatter plot in Fig. 3 illustrated the relationship between Emotional scores and subjectivity scores for all analyzed statements. The plot showed a broad distribution of Emotional scores, ranging from highly negative to highly positive. Most statements had high subjectivity scores, indicating that the nurses’ reflections were predominantly based on personal opinions and feelings rather than objective observations. This highlights the deeply emotional and personal nature of the nurses’ experiences and the variability in their emotional responses to different aspects of their work.

The scatter plot in Fig. 4 explored the relationship between the length of the interview responses and their Emotional scores. The analysis revealed that longer responses tended to have more varied Emotional scores, ranging from highly negative to highly positive. This suggests that longer statements provided more detailed and nuanced reflections, capturing a wider range of emotions. Shorter statements, on the other hand, were more likely to have neutral or moderately positive Emotional scores, indicating more concise and focused responses. This suggests that more detailed reflections, often encompassing various aspects of the nurses’ experiences, provided a richer emotional landscape.

The analysis conducted using ChatGPT-4o revealed several key factors contributing to burnout and turnover among operating room nurses. Emotional challenges were identified as a significant contributor, with participants reporting high levels of stress and emotional exhaustion stemming from the demanding nature of perioperative nursing.

Factors such as long hours, high workloads, and inadequate staffing were frequently cited as primary contributors to burnout. These issues exacerbated emotional strain and reduced nurses’ overall job satisfaction.

Poor communication and a lack of teamwork among surgical team members were commonly mentioned as additional stressors. These relational challenges often hindered effective collaboration, further contributing to workplace dissatisfaction.

Support systems played a crucial role in managing stress. Access to counseling services and peer support groups was associated with lower levels of burnout and greater job satisfaction. Conversely, the absence of such mechanisms was linked to higher turnover intentions among nurses.

Opportunities for professional growth and career advancement were also significant for reducing burnout and turnover. Nurses who had access to these opportunities reported greater job satisfaction and were less likely to consider leaving their positions.

All findings were shared with the participants as part of the study feedback process.

The detailed categorized analysis showed that patient care emerged as a main theme, with nurses frequently discussing their interactions with patients and the emotional challenges they face. The variability in Emotional scores, ranging from positive to negative, highlights the complex nature of patient care in the operating room. These findings align with previous studies that have emphasized the emotional demands of nursing and the impact of patient interactions on nurses’ well-being [3, 4]. Understanding how to transform these negative feelings into positive ones will be crucial in supporting the passion of operating room nurses. Nursing managers, colleagues, and educators need to be involved in ways that improve nurses’ work motivation [14]. Our results align with the existing literature on emotional labour, which highlights the significant emotional challenges faced by nurses and the coping strategies they employ. For instance, the concept of emotional labour, as introduced by Hochschild (1983), underscores the necessity for nurses to manage their emotions to meet the emotional demands of their roles [12]. This is consistent with our findings that operating room nurses experience a range of emotions, including stress, emotional exhaustion, and job satisfaction, which are influenced by their work environment and interprofessional relationships. We have also included references to specific studies that explore emotional regulation and the impact of emotional labour on nurse well-being. For example, Fasbinder et al. (2020) conducted a concept analysis on emotional regulation among nurses, highlighting the importance of self-awareness, control, and proactive thinking in managing emotions. Our study supports these findings, as we observed that nurses utilize various coping strategies to handle the emotional demands of perioperative nursing [5]. Surgical safety was another significant theme, with nurses emphasizing the importance of maintaining a safe surgical environment. The generally positive Emotional scores in this category reflect a strong commitment to safety and professional fulfillment. This is consistent with existing literature that underscores the critical role of safety protocols and effective communication in the operating room [1, 2]. Operating room nurses believe that securing patient safety and preventing mistakes are key elements in their work, and they consider the culture of prevention and protection crucial in enhancing safety [15]. To develop safety-conscious nurses, it is necessary to maintain a high level of teamwork and communication [16]. Safety awareness and operating room nurses’ emotions are connected and need to be valued. Nursing skills involved discussions about the technical and practical aspects of the nurses’ daily routines. The neutral Emotional scores suggest that these tasks are viewed as routine but essential components of their job [17]. This finding highlights the importance of technical proficiency and routine in ensuring the smooth operation of surgical procedures.

The comparison between human analysis and ChatGPT-4o revealed complementary strengths. Human researchers excelled in identifying subtle emotional nuances and context-specific details, such as ambivalent feelings of pride and frustration. ChatGPT, on the other hand, demonstrated efficiency in processing large datasets and identifying broad emotional patterns. However, AI tools often missed subtler nuances that human researchers were more attuned to, such as inferred emotions and complex interrelations. This underscores the value of combining human expertise with AI technology to achieve a comprehensive and nuanced understanding of emotions in perioperative nursing.

The study’s findings highlight the importance of addressing emotional support, effective communication, and safety protocols to prevent nurse turnover and burnout. Emotional support programs, such as counseling and peer support groups, can help nurses manage stress [18]. Effective communication within the surgical team can reduce misunderstandings and improve teamwork [19, 20]. Maintaining robust safety protocols and involving nurses in their development can enhance their sense of control and job satisfaction. Recognizing nurses’ contributions and providing career development opportunities are also crucial for motivation and retention [21]. Promoting work-life balance through flexible scheduling and wellness programs can further prevent burnout. Organizational support, including mental health resources and proactive leadership, is essential to create a supportive work environment [22]. Future research should continue to explore these areas to develop comprehensive interventions for improving nurse well-being and job satisfaction.

This study utilized generative AI, specifically ChatGPT-4o, for Emotional and Subjectivity analysis, providing a detailed and quantitative evaluation of the emotional tone within the interview data. The application of advanced AI tools allowed for a systematic exploration of the emotional experiences of operating room nurses, complementing traditional qualitative methods. The keyword co-occurrence network and cluster analysis provided additional layers of understanding by revealing the interconnected nature of themes and capturing the distinct emotional landscapes expressed by the participants.

Generative AI demonstrates several notable advantages in emotional analysis. First, it offers an objective framework to quantify emotions, reducing potential biases that may arise from manual coding. This is particularly valuable for ensuring consistency across large datasets, as ChatGPT applied scoring metrics uniformly, providing a reproducible basis for emotional evaluation. Additionally, the efficiency of AI in processing extensive text data makes it a practical tool for large-scale studies, enabling the identification of broad emotional patterns and trends that might otherwise require significant time and effort through manual analysis. A scatter plot of text length and emotional scores was particularly insightful, illustrating the range and intensity of emotions experienced by the nurses. This visual analysis highlighted the interrelation of key themes, such as patient care, surgical safety, and nursing skills, emphasizing the interconnected emotional challenges in perioperative nursing. For instance, longer interview responses often captured complex emotional narratives, providing a richer understanding of the participants’ experiences, while shorter responses tended to reflect more focused emotional insights.

Despite these strengths, the role of AI in this study was supplementary rather than primary. ChatGPT’s integration into the analysis aimed to enhance traditional methods rather than replace human judgment. While AI excels in processing data quickly and consistently, it has notable limitations. For example, ChatGPT tends to focus on explicit language and may overlook implicit emotional cues, nuances, or context-specific subtleties that human researchers can discern. This is evident in scenarios where human analysis captured inferred emotions, such as personal responsibility or emotional ambivalence, which were categorized more broadly by ChatGPT. Moreover, AI’s reliance on the quality and representativeness of input data presents a critical limitation. In this study, the sample size was relatively small, comprising 10 participants. While this enabled an in-depth qualitative analysis, it limited the generalizability of the findings. Future studies should incorporate larger and more diverse samples to validate and extend these insights, ensuring that the analysis captures the full spectrum of experiences among operating room nurses.

The combination of AI and human analysis offered a more nuanced understanding of the data, leveraging the strengths of both approaches. Human expertise remains crucial for interpreting complex emotions and contextual subtleties, while AI contributes efficiency, objectivity, and scalability. This synergy is particularly relevant in healthcare, where understanding the emotional dynamics of professionals is essential for improving well-being and patient outcomes. The findings also align with prior research that emphasizes the importance of using appropriate prompts and datasets to maximize AI’s accuracy and minimize errors [22]. Future advancements in generative AI, such as incorporating empathy, emotion recognition, personality assessment, and mental health diagnostics, could significantly enhance its applicability in healthcare [11, 23]. For example, AI tools capable of identifying early warning signs of burnout or emotional distress could support proactive interventions, benefiting both healthcare providers and their organizations.

In conclusion, while generative AI offers valuable contributions to emotional analysis, its integration should be viewed as an enhancement rather than a replacement for human judgment. Combining AI-driven tools with traditional qualitative methods holds the potential to advance emotional analysis across healthcare domains, providing deeper insights into the challenges and experiences of professionals like operating room nurses.