Knowledge, attitudes and practices of Lebanese Medical Students towards Minimally invasive surgery in Lebanon : A cross sectional study

Knowledge, attitudes and practices of Lebanese Medical Students towards Minimally invasive surgery in Lebanon : A cross sectional study | 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 Knowledge, attitudes and practices of Lebanese Medical Students towards Minimally invasive surgery in Lebanon : A cross sectional study Anthony Mina, Tigresse Boutros, Elie Ghadban, Cesar Ghadbane, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6640818/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: As surgical practice evolves with the rise of minimally invasive techniques, medical education struggles to keep pace, especially in resource-constrained settings. In Lebanon, where advanced systems like robotic platforms are available, access to structured training remains limited. Methods: A cross-sectional survey assessed the knowledge, attitudes, and practices related to minimally invasive surgery among 345 Lebanese medical students. Data were collected via an online questionnaire from December 2024 to April 2025. Statistical analysis included descriptive statistics, bivariate tests, and multivariable logistic regression. Results: Participants showed moderate knowledge levels, with higher scores in robotic (76%) than laparoscopic (60.4%) domains. Attitudes toward MIS were overwhelmingly positive (71.3% moderate, 4.9% high), but practical experience was limited (53.9% low practice). Key predictors of high KAP scores included tech-savviness and gender, with female and tech-oriented students showing more engagement. School type also significantly influenced knowledge outcomes: public university students scored higher in laparoscopy, while private university students excelled in robotics. Conclusion: Despite enthusiasm for MIS, practical exposure remains insufficient among Lebanese trainees. This gap indicates the urgent need for equitable, simulation-based training frameworks that can support evolving surgical education in low-resource settings. Early exposure may better prepare future physicians for the evolving landscape of surgical care. Minimally Invasive Surgery Laparoscopy Robotic Surgery Medical Education Lebanon KAP Study Medical Students Introduction Human evolution has long depended on tool-making ( 1 ). From the scalpel to the surgical robot, the operating room stands as a real-life testament of technological imagination. Nowhere is this progression more striking than in the shift from traditional open techniques to minimally invasive surgery (MIS) ( 2 , 3 ). The laparoscopic revolution began with Mouret’s first cholecystectomy in 1987 in France ( 4 , 5 ), quickly gaining attention due to benefits such as shorter recovery time, reduced post-operative pain, and fewer complications ( 4 ). By the 1990s, laparoscopy became standard in many procedures, with over 60% of gallbladder removals in North America done laparoscopically in 1993 ( 6 ). As surgical innovation accelerated, robotic systems soon entered clinical practice, beginning in the early 2000s. These platforms, most notably the da Vinci system, introduced greater precision, 3D visualization, and ergonomic comfort for surgeons ( 7 , 8 ). These advantages have driven rapid uptake of robotic surgery in fields like urology (e.g., for prostatectomy), gynecology, general surgery, and cardiothoracic surgery ( 8 , 9 ), therefore eliminating doubts regarding the clinical benefits of robotic systems. Notably, the evolution of laparoscopic and robotic surgery in Lebanon mirrored global trends, with major academic hospitals introducing MIS in the early 1990s and quickly expanding its use across various subspecialties and complex procedures ( 10 , 11 ). Yet, little was known about how best to train surgeons to master them ( 12 ). In other words, this evolution in technique has not been matched by an equivalent evolution in education ( 12 , 13 ). As the nature of surgery changes, the methods of surgical education must evolve as well. Traditional training models often fail to prepare students for the unique challenges of MIS, which demands excellent hand-eye coordination, spatial orientation, and familiarity with sophisticated instruments ( 14 – 16 ). In response, many countries have developed structured simulation-based training programs, including the widely adopted Fundamentals of Laparoscopic Surgery (FLS), which offers hands-on skill development in a safe, standardized setting ( 17 , 18 ). Specifically, Shahrezaei et al. confirm that programs like FLS effectively enhance surgical training through structured, simulation-based methods ( 18 ). Despite these advances, access to such training remains uneven, particularly in low- and middle-income countries ( 19 , 20 ). Across the Middle East and North Africa region, students frequently encounter limited opportunities to engage with laparoscopic techniques and even fewer with robotic systems ( 19 ). A 2021 systematic review by Wilkinson et al. identified seven core barriers to sustainable MIS training in such settings: inadequate funding, limited equipment and maintenance, lack of local expertise, rigid curricula, weak institutional support, scarce clinical opportunities, and underdeveloped training frameworks ( 19 ). Indeed, a number of international studies examined how medical trainees understand and engage with MIS, focusing on their knowledge, attitudes, and practices (KAP) ( 21 – 23 ). These studies show a clear pattern: students and junior doctors are generally positive about MIS and interested in learning more, but they often lack proper training and hands-on experience ( 21 – 23 ). In Saudi Arabia, for instance, only a minority of medical students report any formal exposure to robotic surgery, though most express a strong desire to learn it, as observed by Sultan et al.( 21 ). Similarly, in the United Kingdom, studies show that undergraduate students value laparoscopic experience even when they do not intend to pursue surgery as a career, indicating the growing relevance of these techniques across medical disciplines ( 23 ). Early exposure to MIS has been linked to increased interest in surgery, improved simulator performance, and greater procedural confidence, making a compelling case for its inclusion in undergraduate curricula ( 21 – 23 ). Among junior residents, this gap becomes even more pronounced, with many entering practices underprepared and reliant on ad hoc learning ( 24 ). Mullen et al. point out that, while minimally invasive techniques are a win for patient outcomes, they often leave junior residents with fewer opportunities to get their hands dirty in traditional surgeries ( 25 ). As a result, they end up relying on informal learning and learning as they go. Similarly, Mattar et al. echo this, revealing that residency programs are often not giving residents the practical experience they need to thrive in fellowships ( 26 ). Both studies make it clear: while the surgical landscape is evolving, the training is definitely not catching-up. The Present Study This study was designed in response to the widening gap between surgical innovation and educational access in Lebanon. Despite the presence of advanced surgical systems, opportunities to engage meaningfully with laparoscopic and robotic surgery remain limited for the majority of medical trainees. This is not solely a matter of technology, but of systemic strain. The combined impact of the country’s financial collapse, prolonged political instability, the COVID-19 pandemic, and the catastrophic Beirut port explosion has destabilized every pillar of the healthcare system, from infrastructure and staffing to clinical exposure and academic continuity ( 27 , 28 ), and this rise in poverty might translate in the increasing difficulty of accessing simulation labs, surgical observership, and faculty mentorship which are critical to developing MIS competency ( 19 , 29 ). The disconnect between technological availability and trainee access, in a country that historically led regional adoption, denotes the urgency of this inquiry. The aim of this study is to fill the major existing gap in the literature about the current state of knowledge, attitudes, and practices related to MIS among Lebanese medical students, and to identify the institutional and personal factors that influence these outcomes. By framing the issue within Lebanon’s complex socioeconomic and academic landscape, this study offers insight into a broader regional challenge: how can surgical education in resource-constrained environments keep pace with global advancements? Methods Study Design and Participants A cross-sectional design was used to assess the knowledge, attitudes, and practices related to MIS among medical students in Lebanon. The study was conducted over a five-month period from December 2024 to April 2025 and targeted individuals enrolled in recognized Lebanese medical schools. Eligibility criteria included participants from all academic stages, spanning pre-clinical years (MED I & II), clinical years (MED III & IV). The sampling strategy followed a “snowball” approach across medical faculties, to maximize geographic and institutional representation across both public and private universities. Sample Size Calculation The minimum sample size was calculated using the Epi Info™ StatCalc tool (Centers for Disease Control and Prevention, USA) ( 30 ). Assuming a total population of approximately 2,800 medical students in Lebanon, with a confidence level of 95%, a margin of error of 5%, and an expected response distribution of 50%, the minimum sample size was estimated at 338 participants. A total of 346 responses were collected, meeting the threshold for statistical representativeness. Questionnaire and Data Collection Data collection was carried out through an online questionnaire via Google Forms. Prior to participation, digital informed consent was required. If consent was not granted, the form automatically exited without recording any data. Only one participant declined to provide consent and was automatically excluded by the website hosting the questionnaire. The questionnaire was formed using items taken from a published study and the remaining items were developed by the investigating team ( 21 ). The questionnaire is available as supplementary file to this paper. The questionnaire began with an introductory section detailing the study’s purpose, the research team, and confidentiality assurances, concluding with the consent request. The next section gathered sociodemographic information such as age (via date of birth), gender, year of study, type of medical school (public or private), intended specialty, and participants’ self-rated technological proficiency. Subsequent sections assessed participants' knowledge, attitudes, and practices regarding MIS. Laparoscopic knowledge was evaluated using eleven items covering procedural principles and instrumentation, while robotic surgery knowledge was assessed using thirteen items focusing on system features and clinical indications. A single item asked participants to identify their main sources of MIS knowledge. Practices were evaluated through six items related to prior exposure to training or surgical observation. Finally, a three-item section explored perceived barriers to implementing robotic surgery in Lebanon and participants' views on its curricular integration. Statistical Analysis Data were analyzed using IBM SPSS Statistics version 26.0 and Python 3.11. Descriptive statistics were computed for all variables in the study. Knowledge, attitude, and practice scores were calculated based on predefined coding criteria. The practice score, ranging from 0 to 6, was derived from six binary items and recoded into a binary outcome (low vs. moderate-to-high) for regression analysis. Bivariate analyses included chi-square tests and t-tests to explore associations between KAP scores and demographic variables. Binary logistic regression was then performed to identify independent predictors of moderate-to-high practice. A significance threshold was set at p < 0.05. Results Descriptive Analysis A total of 345 medical students participated in the study. The mean age of respondents was 23.0 ± 2.3 years. Of the total participants, 198 (57.4%) were male and 147 (42.6%) were female. Other sociodemographic factors are represented in Table 1 . Table 1 Socio-demographic factors of participants Variable Value Total participants, N 345 Mean age, y (SD) 23.0 (2.3) Gender, N (%) Male 198 (57.4) Female 147 (42.6) Tech-savvy respondents, N (%) 183 (53.0) Type of medical school, N (%) Public university 150 (43.5) Private university 195 (56.5) Intended specialty, N (%) Medical specialty 233 (67.5) Surgical specialty 112 (32.5) Participants demonstrated higher average scores in robotic compared to laparoscopic knowledge. When categorized, most participants fell into the moderate knowledge group. Despite generally favorable attitudes toward MIS, reported practice was limited, indicating a gap between disposition and exposure. Details are represented in Tables 2 and 3 . Table 2 Descriptive statistics of knowledge scores Score Type Max Possible Score Mean SD Min Max Mean (%) Laparoscopic Knowledge 11 6.64 1.58 1 9 60.4 Robotic Knowledge 13 9.88 1.79 2 13 76.0 Table 3 Distribution of laparoscopic and robotic knowledge levels Knowledge Domain Level N % Laparoscopic Knowledge Low 102 29.6 Moderate 153 44.3 High 90 26.1 Robotic Knowledge Low 99 28.7 Moderate 150 43.5 High 96 27.8 Attitude scores showed that a majority of participants held positive perceptions of MIS integration in medical training. However, practice scores indicated limited engagement with relevant training or exposure opportunities. Details are represented in Tables 4 and 5 . Table 4 Descriptive summary of attitude and practice scores Score Type Mean SD Min Max Attitude 3.4 1.0 0 5 Practice 1.6 1.2 0 6 Table 5 Attitude and practice score levels Domain Level N % Attitude Low 82 23.8 Moderate 246 71.3 High 17 4.9 Practice Low 186 53.9 Moderate 135 39.1 High 24 7.0 Bivariate Analysis Chi-square analysis revealed several statistically significant associations. Laparoscopic knowledge was significantly associated with school type (p = 0.004), where public university students were more likely to achieve high scores. Robotic knowledge was also associated with school type (p = 0.043), with higher scores seen among students from private institutions. Attitude was significantly associated with intended specialty (p < 0.001) and tech-savviness (p = 0.027), both linked to more favorable perceptions. Practice score was significantly associated with gender (p < 0.001), school type (p = 0.039), future specialty (p = 0.001), and tech-savviness (p < 0.001). Details are represented in Table 6 . Table 6 Bivariate associations between participant characteristics and high knowledge, attitude, and practice scores Note: Only predictors with statistically significant associations (p < 0.05) are presented. Domain Predictor χ² p-value Laparoscopic Knowledge School Type 10.99 0.004 Robotic Knowledge School Type 6.30 0.043 Attitude Future Specialty 29.38 < 0.001 Tech-savviness 7.21 0.027 Practice Gender 25.50 < 0.001 School Type 6.50 0.039 Future Specialty 13.41 0.001 Tech-savviness 19.34 < 0.001 Multivariable Analysis Logistic regression confirmed several independent predictors of high performance. For laparoscopic knowledge, school type remained significant, with students from private institutions less likely to score in the top tertile (p < 0.001). In robotic knowledge, private institution students had greater odds of high performance (p = 0.042). High attitude scores were independently predicted by tech-savviness (p = 0.001), and high practice scores were significantly predicted by both tech-savviness (p = 0.002) and gender (p = 0.002), with female students reporting greater engagement. Details are represented in Table 7 . Table 7 Multivariable Logistic Regression Identifying Independent Predictors of High Knowledge, Attitude, and Practice Scores Note: Only predictors with statistically significant associations (p < 0.05) are presented. Domain Predictor OR 95% CI p-value Laparoscopic Knowledge School Type 0.40 0.25–0.65 < 0.001 Robotic Knowledge School Type 2.36 1.03–5.38 0.042 Attitude Tech-savviness 4.42 1.81–10.77 0.001 Practice Gender (Female) 0.25 0.10–0.60 0.002 Tech-savviness 5.63 1.90–16.72 0.002 Additional Analysis Cluster analysis revealed three respondent profiles. One group showed strong knowledge but low practice; another displayed low scores across all domains; and a third demonstrated high attitude and practice scores despite only moderate knowledge. These clusters reveal variability in learning engagement among participants. Details are represented in Table 8 . Table 8 Cluster group analysis (K-Means, K = 3) Cluster N Laparoscopic Knowledge Robotic Knowledge Attitude Practice 0 85 7.4 11.5 3.59 1.25 1 88 6.1 8.4 1.98 0.78 2 172 6.5 8.0 4.05 1.99 Discussion This study aimed to assess the knowledge, attitudes, and practices of medical trainees in Lebanon regarding minimally invasive surgery, and the findings revealed a striking paradox: although students expressed strong enthusiasm and positive attitudes toward MIS, particularly robotic surgery, their practical exposure and foundational knowledge, especially in laparoscopy, remained limited. Knowledge The results of this study reveal a moderately strong base of knowledge about minimally invasive surgery among Lebanese medical trainees, particularly regarding robotic techniques. This finding is somewhat surprising, given that laparoscopy has been in widespread surgical practice for a longer history ( 31 , 32 ) and is typically more accessible in training settings than robotics ( 33 ). Nevertheless, a closer look reveals that approximately one-third of students scored in the low knowledge tier for each modality, indicating that significant knowledge gaps persist ( 21 ). These findings align with international reports that formal curricula often underrepresent MIS, especially robotics. In a Saudi Arabian study, only 22.6% of students had any formal exposure to robotic surgery, with most relying on informal sources ( 21 ). The particularly higher robotic knowledge scores may reflect a growing fascination with emerging technology and a tendency among students to engage with digital resources and global surgical trends through online platforms, rather than through hands-on clinical exposure. While this trend suggests that students are proactively seeking information about innovative surgical modalities, it also points to a relative underrepresentation of basic laparoscopic education within current training programs ( 22 , 34 , 35 ). The literature explicitly discusses the importance of introducing fundamental MIS content into undergraduate medical education, including an introduction to operative techniques, instrumentation, and clinical applications ( 22 , 34 , 35 ), and even brief exposure, such as a 40-minute session, has been shown to boost student confidence in discussing robotic procedures, in a study by Naik and Mandal ( 23 ). Type of medical school as a predictor A notable predictor of MIS knowledge in this study was the type of medical school attended. Institutional affiliation emerged as a significant factor influencing knowledge outcomes, with public university students demonstrating stronger performance in laparoscopic knowledge and private university students showing higher scores in robotic surgery. This distinction reflects differences in curricular focus, and access to technology between institutions. Public universities may focus on traditional operative training, particularly in laparoscopy, as part of a structured general surgery curriculum. In contrast, private institutions, which are often affiliated with better-resourced hospitals, may offer more observational exposure to robotic systems. This finding aligns with findings from an Indian survey that reported none of the government medical colleges had access to robotic platforms in contrast to the private sector ( 36 ). Similarly, a systematic review of robotic surgery training programs revealed significant disparities in implementation across institutions and called for standardization to ensure equitable training opportunities ( 37 ). These findings suggest that institutional context, particularly access to surgical equipment and infrastructure, plays a critical role in shaping both practical experience and theoretical knowledge in MIS. Attitude Across the sample, participants expressed generally positive attitudes regarding MIS and its role in their education. Most respondents agreed with the value of acquiring laparoscopic and robotic surgery skills, and a considerable proportion expressed willingness to pursue training opportunities if available with only a minority scoring low on the attitude scores. This reflects broad recognition among future doctors that MIS is an important and worthwhile aspect of modern medicine. Such positive attitudes are consistent with findings from other regions. A survey in Saudi Arabia reported that nearly two-thirds of medical students had a positive outlook on robotic surgery, with many believing it would improve patient outcomes and supporting national investment in robotic services ( 21 ). Similarly, a recent U.S. study found that 72% of medical students, regardless of intended specialty, supported learning about robotic surgery during undergraduate training, reminding that MIS is a core component of modern clinical competence ( 38 ). Tech-Savviness as a predictor Interestingly, tech-savviness emerged as a significant independent predictor in multivariable analysis. This association suggests the crucial role of digital literacy in shaping medical trainees' perceptions. Trainees comfortable with digital tools are more likely to adopt the sophisticated technologies central to MIS, including robotic-assisted systems and advanced imaging modalities. This observation aligns with evidence showing that digital proficiency enhances both confidence and competence in using innovative surgical techniques ( 39 , 40 ). In fact, simulation-based and virtual reality training programs have been shown to significantly improve operative performance and cognitive readiness, reinforcing the value of technological fluency in surgical education ( 39 , 40 ). Although students expressing interest in surgical careers initially demonstrated more favorable attitudes toward MIS in bivariate analysis, this association did not persist after adjusting for covariates. This attenuation suggests that technological orientation, rather than career interest alone, may drive these attitudes. Supporting this, MacNevin et al. found that students with higher technology readiness were more likely to express interest in both technology-focused and surgical specialties, suggesting a shared inclination toward innovation and technical complexity ( 41 ). Practice While knowledge and attitudes were generally encouraging, actual practical exposure to MIS among surveyed students was limited. Most students had limited hands-on experience with laparoscopic or robotic equipment, and only a small fraction had participated in simulation-based activities or operating room observations. This discrepancy between enthusiasm and exposure reflects a familiar challenge in surgical education, particularly in low- and middle-income countries ( 19 , 38 , 42 ). For instance, a study in Nigeria by Ijah and Manuel revealed that while medical doctors expressed favorable views toward laparoscopic surgery, most lacked hands-on experience and access to appropriate training resources, which reminds the ongoing disconnect between enthusiasm and opportunity in settings with limited resources ( 42 ). Tech-Savviness and female gender as predictors Bivariate analyses identified several demographic and educational factors associated with higher practical exposure to MIS, including gender, school type, future specialty choice, and tech-savviness. However, when controlling for potential confounders in multivariable logistic regression, only gender and tech-savviness were retained as significant independent predictors. First, tech-savvy students were more likely to report higher participation in MIS-related activities. This supports prior research indicating that comfort with digital tools and virtual environments may enhance engagement with simulation-based or technologically advanced training modalities. Indeed, there is evidence that medical trainees who play video games have superior performance on laparoscopic simulators, completing tasks faster and with fewer errors compared to those without gaming experience ( 43 ). Gender differences also emerged, with female students reporting greater practice exposure than their male peers. This finding is surprising and challenges traditional assumptions about gender dynamics in surgical education, likely reflecting increasing proactivity among women pursuing surgical careers. This pattern may reflect a broader response to structural barriers in surgical education. Female students, particularly in contexts with limited opportunities, may be taking deliberate steps to build their experience and visibility in the field. As Bruce et al. suggest, many women in surgical training face gender-based discrimination, necessitating exceeding expectations in order to gain recognition ( 44 ). Complementing this, Burgos and Josephson found that gender differences in the teaching and learning of surgery often drive female learners to adopt more intentional and proactive strategies to succeed ( 45 ). Thus, this constellation of findings suggests that in constrained training environments, motivated female students may be particularly driven to seek out hands-on experiences, build skills, and increase their presence in the surgical field ( 44 , 45 ). Interestingly, while intended future specialty and school type were associated with practice scores in bivariate analysis, neither remained significant in the adjusted model. This attenuation suggests that their effects may be mediated by underlying factors such as tech-savviness and possibly individual initiative. This attenuation suggests that their effects may be mediated by underlying factors such as tech-savviness or individual initiative. For example, students attending private institutions may benefit from greater access to robotic platforms and simulation resources due to institutional affiliations with high-tech centers and substantially higher incomes ( 46 , 47 ). However, it is likely that only those who are technologically inclined or proactive in seeking these opportunities are able to turn access into actual practice ( 43 ). Similarly, students planning surgical careers may also self-identify as tech-savvy ( 48 ), engage more readily with digital tools ( 49 ), and actively seek simulation or operating room experiences ( 50 ). In both cases, these findings reinforce the idea that individual-level traits may bridge or even outweigh structural differences in training exposure. Clinical Implications The findings of this study carry significant implications for both curriculum development and policy. First, there is a clear need to introduce MIS concepts and basic skills earlier in the medical education pathway, ideally beginning in the clinical years. Simulation-based training, even with low-cost box trainers or virtual modules, could offer students a safe and standardized environment to build confidence and competence before progressing to the operating room. Institutions with access to high-end robotic systems should make deliberate efforts to democratize exposure, ensuring that access is not limited to select students or elective rotations. Partnerships between universities and national surgical centers could facilitate workshops, boot camps, or certificate programs that increase exposure without placing excessive demands on limited hospital resources. At a policy level, educational authorities and accreditation bodies should consider establishing national guidelines for minimal MIS exposure by graduation, particularly as these techniques become standard components of clinical care. Moreover, mentorship programs involving MIS-trained faculty could support longitudinal skill development, while helping to further enhance interest in surgical careers and promote gender equity. Limitations This study is subject to several limitations. As a cross-sectional survey, it captures attitudes and practices at a single point in time and cannot establish causal relationships. The reliance on self-reported data introduces potential recall and social desirability biases, particularly regarding knowledge accuracy and practice engagement. The snowball sampling approach, although effective in reaching a diverse range of institutions, may have excluded students without access to digital networks or those less engaged with surgical topics, leading to selection bias. Furthermore, variation in robotic exposure is likely influenced by institutional affiliation and may not reflect broader accessibility trends. Despite these limitations, the study provides a valuable snapshot of the current state of MIS preparedness in Lebanon and highlights clear opportunities for improvement in training infrastructure and curriculum design. Conclusion This study reveals that while Lebanese medical students exhibit positive attitudes toward minimally invasive surgery, major disparities exist in their knowledge and practical exposure, particularly regarding robotic surgery. Factors such as tech-savviness, gender, institutional type, and career orientation influenced KAP outcomes. Despite broad support for integrating MIS into medical education, opportunities for hands-on training remains limited. Hence, there is an urgent need to implement structured, equitable, and simulation-based MIS curricula across all Lebanese medical schools. Doing so will ensure that future physicians are adequately prepared for the evolving landscape of modern surgical care. Declarations Ethical Approval This study received ethical clearance from the Ethics Committee of Notre Dame Des Secours University Hospital (NDS–UH) (Reference: CR1/2025). After presenting the study objectives and investigators, informed consent was obtained from all the participants before proceeding with the questionnaire. Failure to provide consent led to automatic exit from the questionnaire. This study adhered to the Helsinki Declaration for medical research involving human participants. Consent for publication : All authors give their consent for publication. Clinical trial number: Not applicable Availability of data and materials: Data is provided within the manuscript or supplementary information files. Competing interests: None Funding declaration: No funding was needed for this study Authors contributions : A.M wrote the manuscript and created the study design; T.B edited the manuscript; E.G,C.G&J.T contributed in data collection ; J.G supervised the study and reviewed the manuscript before submission Acknowledgements : None References Stout D, Chaminade T. The evolutionary neuroscience of tool making. Neuropsychologia. 2007;45(5):1091–100. Arezzo A. The past, the present, and the future of minimally invasive therapy in laparoscopic surgery: a review and speculative outlook. Minim Invasive Ther Allied Technol MITAT Off J Soc Minim Invasive Ther. 2014;23(5):253–60. Harrell AG, Heniford BT. Minimally invasive abdominal surgery: lux et veritas past, present, and future. Am J Surg. 2005;190(2):239–43. Jaffray B. Minimally invasive surgery. Arch Dis Child. 2005;90(5):537–42. Soper NJ. Laparoscopic Cholecystectomy. In: Current Review of Minimally Invasive Surgery [Internet]. Springer, York N. NY; 1998 [cited 2025 May 4]. pp. 1–12. Available from: https://link.springer.com/chapter/10.1007/978-1-4612-1692-6_1 Urbach DR, Stukel TA. Rate of elective cholecystectomy and the incidence of severe gallstone disease. CMAJ Can Med Assoc J. 2005;172(8):1015–9. Yates DR, Vaessen C, Roupret M. From Leonardo to da Vinci: the history of robot-assisted surgery in urology. BJU Int. 2011;108(11):1708–13. Hockstein NG, Gourin CG, Faust RA, Terris DJ. A history of robots: from science fiction to surgical robotics. J Robot Surg. 2007;1(2):113–8. Brassetti A, Ragusa A, Tedesco F, Prata F, Cacciatore L, Iannuzzi A, et al. Robotic Surgery in Urology: History from PROBOT® to HUGOTM. Sensors. 2023;23(16):7104. Geagea T. Laparoscopic cholecystectomy: initial experience in Lebanon. J Med Liban. 1992;40(2):77–80. Khauli RB, Traboulsi SL, Medawar W, Abu Dargham R, Abdelnoor AM, Hussein MK. Laparoscopic donor nephrectomy: The Middle East experience. Arab J Urol. 2012;10(1):46–55. Reitano E, Famularo S, Dallemagne B, Mishima K, Perretta S, Riva P, et al. Educational Scoring System in Laparoscopic Cholecystectomy: Is It the Right Time to Standardize? Med Kaunas Lith. 2023;59(3):446. Ali SA, Soomro AG, Mohammad AT, Jarwar M, Siddique AJ. Experience of laparoscopic cholecystectomy during a steep learning curve at a university hospital. J Ayub Med Coll Abbottabad JAMC. 2012;24(1):27–9. Brian R, Davis G, Park KM, Alseidi A. Evolution of laparoscopic education and the laparoscopic learning curve: a review of the literature. Laparosc Surg. 2022;6:34–34. Hong M, Rozenblit JW, Hamilton AJ. Simulation-based surgical training systems in laparoscopic surgery: a current review. Virtual Real. 2021;25(2):491–510. Beyer L, Troyer JD, Mancini J, Bladou F, Berdah SV, Karsenty G. Impact of laparoscopy simulator training on the technical skills of future surgeons in the operating room: a prospective study. Am J Surg. 2011;202(3):265–72. Peters JH, Fried GM, Swanstrom LL, Soper NJ, Sillin LF, Schirmer B, et al. Development and validation of a comprehensive program of education and assessment of the basic fundamentals of laparoscopic surgery. Surgery. 2004;135(1):21–7. Shahrezaei A, Sohani M, Taherkhani S, Zarghami SY. The impact of surgical simulation and training technologies on general surgery education. BMC Med Educ. 2024;24(1):1297. Wilkinson E, Aruparayil N, Gnanaraj J, Brown J, Jayne D. Barriers to training in laparoscopic surgery in low- and middle-income countries: A systematic review. Trop Doct. 2021;51(3):408–14. Chao TE, Mandigo M, Opoku-Anane J, Maine R. Systematic review of laparoscopic surgery in low- and middle-income countries: benefits, challenges, and strategies. Surg Endosc. 2016;30(1):1–10. Sultan I, Bardi MF, Baatta AM, Almaghrabi S, Mohammed RA. Medical Students’ Attitude Towards Robotic Surgery: A Cross-Sectional Survey. J Med Educ Curric Dev. 2022;9:23821205211066483. Moglia A, Sinceri S, Ferrari V, Ferrari M, Mosca F, Morelli L. Proficiency-based training of medical students using virtual simulators for laparoscopy and robot-assisted surgery: results of a pilot study. Updat Surg. 2018;70(3):401–5. Naik R, Mandal I. Robotic simulation experience in undergraduate medical education: a perspective. J Robot Surg. 2020;14(5):793–4. Borahay MA, Haver MC, Eastham B, Patel PR, Kilic GS. Modular comparison of laparoscopic and robotic simulation platforms in residency training: a randomized trial. J Minim Invasive Gynecol. 2013;20(6):871–9. Mullen MG, Salerno EP, Michaels AD, Hedrick TL, Sohn MW, Smith PW, et al. Declining operative experience for junior level residents: Is this an unintended consequence of minimally invasive surgery? J Surg Educ. 2016;73(4):609–15. Mattar SG, Alseidi AA, Jones DB, Jeyarajah DR, Swanstrom LL, Aye RW, et al. General surgery residency inadequately prepares trainees for fellowship: results of a survey of fellowship program directors. Ann Surg. 2013;258(3):440–9. Itani R, Karout L, Kassab MB, Karout S, Rabah M, Mais HEMHE, et al. A crisis amidst many others: COVID-19 response satisfaction during the economic collapse and post-Beirut port explosion in Lebanon: 10.55131/jphd/2024/220102. J Public Health Dev. 2024;22(1):12–21. Bou Sanayeh E, El Chamieh C. The fragile healthcare system in Lebanon: sounding the alarm about its possible collapse. Health Econ Rev. 2023;13:21. Azadi S, Green IC, Arnold A, Truong M, Potts J, Martino MA. Robotic Surgery: The Impact of Simulation and Other Innovative Platforms on Performance and Training. J Minim Invasive Gynecol. 2021;28(3):490–5. Epi Info™ | CDC [Internet]. 2025 [cited 2025 May 10]. Available from: https://www.cdc.gov/epiinfo/index.html Valle RF, Reichert JA. Laparoscopic Surgery: An Evolving Revolution. JSLS. 2001;5(1):95–6. Lane T. A short history of robotic surgery. Ann R Coll Surg Engl. 2018;100(6sup):5–7. Rocha MFH, Coelho RF, Branco AW, Filgueira PH, de O, Guida R. A census of laparoscopic and robotic urological practice: a survey of minimally invasive surgery department of the Brazilian Society of Urology. Int Braz J Urol Off J Braz Soc Urol. 2019;45(4):732–8. Doumat G, Daher D, Ghanem NN, Khater B. Knowledge and attitudes of medical students in Lebanon toward artificial intelligence: A national survey study. Front Artif Intell. 2022;5:1015418. Orlando MS, Thomaier L, Abernethy MG, Chen CCG. Retention of laparoscopic and robotic skills among medical students: a randomized controlled trial. Surg Endosc. 2017;31(8):3306–12. Darlington D, Anitha FS, Joseph C. A Cross-Sectional Study of Resident Training in Robotic Surgery in India. Cureus 14(2):e22162. Rahimi AO, Ho K, Chang M, Gasper D, Ashouri Y, Dearmon-Moore D, et al. A systematic review of robotic surgery curricula using a contemporary educational framework. Surg Endosc. 2023;37(4):2833–41. Vigran HJ, Diaz S, Suryadevara A, Blair Y, Aubry S, Reddy RM. Assessing the need for robotic surgery training in standard medical education: insights from medical students. Discov Educ. 2024;3(1):1–6. Seymour NE, Gallagher AG, Roman SA, O’Brien MK, Bansal VK, Andersen DK, et al. Virtual Reality Training Improves Operating Room Performance. Ann Surg. 2002;236(4):458–64. Aydın A, Ahmed K, Abe T, Raison N, Van Hemelrijck M, Garmo H, et al. Effect of Simulation-based Training on Surgical Proficiency and Patient Outcomes: A Randomised Controlled Clinical and Educational Trial. Eur Urol. 2022;81(4):385–93. MacNevin W, Poon E, Skinner TA. Technology readiness of medical students and the association of technology readiness with specialty interest. Can Med Educ J. 2021;12(2):e31–41. Ijah RFOA, Manuel AD. Knowledge, attitude and practice of laparoscopic surgery among medical doctors in Port Harcourt, Nigeria. Int Surg J. 2020;7(4):970–6. Ou Y, McGlone ER, Camm CF, Khan OA. Does playing video games improve laparoscopic skills? Int J Surg Lond Engl. 2013;11(5):365–9. Bruce AN, Battista A, Plankey MW, Johnson LB, Marshall MB. Perceptions of gender-based discrimination during surgical training and practice. Med Educ Online. 2015;20:25923. Burgos CM, Josephson A. Gender differences in the learning and teaching of surgery: a literature review. Int J Med Educ. 2014;5:110–24. Schlottmann F, Patti MG. Novel simulator for robotic surgery. J Robot Surg. 2017;11(4):463–5. Rehman S, Raza SJ, Stegemann AP, Zeeck K, Din R, Llewellyn A, et al. Simulation-based robot-assisted surgical training: a health economic evaluation. Int J Surg Lond Engl. 2013;11(9):841–6. Reimberg J, Lopes LR, Passeri SMRR, Menezes FH. The electronic media and the study profile of the surgical resident. Rev Col Bras Cir. 2021;48:e20212941. Neumeister MW. Technology and Education: The Future of Plastic Surgery Training. Plast Reconstr Surg Glob Open. 2016;4(6):e777. Cardoso SA, Suyambu J, Iqbal J, Cortes Jaimes DC, Amin A, Sikto JT et al. Exploring the Role of Simulation Training in Improving Surgical Skills Among Residents: A Narrative Review. Cureus 15(9):e44654. Additional Declarations No competing interests reported. Supplementary Files KAPquestionnaireadaptedtoLebanonGoogleForms.pdf Cite Share Download PDF Status: Posted Version 1 posted 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6640818","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":475679394,"identity":"197c3393-fc05-43e8-842f-815213c78d76","order_by":0,"name":"Anthony Mina","email":"data:image/png;base64,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","orcid":"","institution":"Holy Spirit University of Kaslik","correspondingAuthor":true,"prefix":"","firstName":"Anthony","middleName":"","lastName":"Mina","suffix":""},{"id":475679395,"identity":"7b555af8-b890-40df-8ef9-0bdf752df153","order_by":1,"name":"Tigresse Boutros","email":"","orcid":"","institution":"Holy Spirit University of Kaslik","correspondingAuthor":false,"prefix":"","firstName":"Tigresse","middleName":"","lastName":"Boutros","suffix":""},{"id":475679396,"identity":"c814827c-3cf2-4c2a-a456-a26665c596e8","order_by":2,"name":"Elie Ghadban","email":"","orcid":"","institution":"Holy Spirit University of Kaslik","correspondingAuthor":false,"prefix":"","firstName":"Elie","middleName":"","lastName":"Ghadban","suffix":""},{"id":475679397,"identity":"db1f4b1f-ad21-4865-9605-7e92908b271c","order_by":3,"name":"Cesar Ghadbane","email":"","orcid":"","institution":"University of Balamand","correspondingAuthor":false,"prefix":"","firstName":"Cesar","middleName":"","lastName":"Ghadbane","suffix":""},{"id":475679398,"identity":"678159d9-1a63-44bd-b946-df294c0ea2f1","order_by":4,"name":"Jana Tahan","email":"","orcid":"","institution":"Lebanese University","correspondingAuthor":false,"prefix":"","firstName":"Jana","middleName":"","lastName":"Tahan","suffix":""},{"id":475679399,"identity":"bece8e13-1f6a-4cf4-ace5-c661de188c1d","order_by":5,"name":"Joseph Gharios","email":"","orcid":"","institution":"Saint Joseph University","correspondingAuthor":false,"prefix":"","firstName":"Joseph","middleName":"","lastName":"Gharios","suffix":""}],"badges":[],"createdAt":"2025-05-11 16:53:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6640818/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6640818/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":92526777,"identity":"bab08b9e-2365-48ae-b5b1-40658dc8af60","added_by":"auto","created_at":"2025-09-30 15:53:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":813901,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6640818/v1/b4926dce-edfc-4caa-900f-b91a22523ccd.pdf"},{"id":85483970,"identity":"6d27ae70-f5e6-4e17-aeb7-87c9c6914774","added_by":"auto","created_at":"2025-06-26 11:37:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":302296,"visible":true,"origin":"","legend":"","description":"","filename":"KAPquestionnaireadaptedtoLebanonGoogleForms.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6640818/v1/56739ba6db1b16c346b8f45c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Knowledge, attitudes and practices of Lebanese Medical Students towards Minimally invasive surgery in Lebanon : A cross sectional study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHuman evolution has long depended on tool-making (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). From the scalpel to the surgical robot, the operating room stands as a real-life testament of technological imagination. Nowhere is this progression more striking than in the shift from traditional open techniques to minimally invasive surgery (MIS) (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe laparoscopic revolution began with Mouret’s first cholecystectomy in 1987 in France (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), quickly gaining attention due to benefits such as shorter recovery time, reduced post-operative pain, and fewer complications (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). By the 1990s, laparoscopy became standard in many procedures, with over 60% of gallbladder removals in North America done laparoscopically in 1993 (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). As surgical innovation accelerated, robotic systems soon entered clinical practice, beginning in the early 2000s. These platforms, most notably the da Vinci system, introduced greater precision, 3D visualization, and ergonomic comfort for surgeons (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). These advantages have driven rapid uptake of robotic surgery in fields like urology (e.g., for prostatectomy), gynecology, general surgery, and cardiothoracic surgery (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), therefore eliminating doubts regarding the clinical benefits of robotic systems. Notably, the evolution of laparoscopic and robotic surgery in Lebanon mirrored global trends, with major academic hospitals introducing MIS in the early 1990s and quickly expanding its use across various subspecialties and complex procedures (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Yet, little was known about how best to train surgeons to master them (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn other words, this evolution in technique has not been matched by an equivalent evolution in education (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). As the nature of surgery changes, the methods of surgical education must evolve as well. Traditional training models often fail to prepare students for the unique challenges of MIS, which demands excellent hand-eye coordination, spatial orientation, and familiarity with sophisticated instruments (\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e–\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). In response, many countries have developed structured simulation-based training programs, including the widely adopted Fundamentals of Laparoscopic Surgery (FLS), which offers hands-on skill development in a safe, standardized setting (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Specifically, Shahrezaei et al. confirm that programs like FLS effectively enhance surgical training through structured, simulation-based methods (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite these advances, access to such training remains uneven, particularly in low- and middle-income countries (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Across the Middle East and North Africa region, students frequently encounter limited opportunities to engage with laparoscopic techniques and even fewer with robotic systems (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). A 2021 systematic review by Wilkinson et al. identified seven core barriers to sustainable MIS training in such settings: inadequate funding, limited equipment and maintenance, lack of local expertise, rigid curricula, weak institutional support, scarce clinical opportunities, and underdeveloped training frameworks (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIndeed, a number of international studies examined how medical trainees understand and engage with MIS, focusing on their knowledge, attitudes, and practices (KAP) (\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e–\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). These studies show a clear pattern: students and junior doctors are generally positive about MIS and interested in learning more, but they often lack proper training and hands-on experience (\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e–\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). In Saudi Arabia, for instance, only a minority of medical students report any formal exposure to robotic surgery, though most express a strong desire to learn it, as observed by Sultan et al.(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Similarly, in the United Kingdom, studies show that undergraduate students value laparoscopic experience even when they do not intend to pursue surgery as a career, indicating the growing relevance of these techniques across medical disciplines (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Early exposure to MIS has been linked to increased interest in surgery, improved simulator performance, and greater procedural confidence, making a compelling case for its inclusion in undergraduate curricula (\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e–\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAmong junior residents, this gap becomes even more pronounced, with many entering practices underprepared and reliant on ad hoc learning (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Mullen et al. point out that, while minimally invasive techniques are a win for patient outcomes, they often leave junior residents with fewer opportunities to get their hands dirty in traditional surgeries (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). As a result, they end up relying on informal learning and learning as they go. Similarly, Mattar et al. echo this, revealing that residency programs are often not giving residents the practical experience they need to thrive in fellowships (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Both studies make it clear: while the surgical landscape is evolving, the training is definitely not catching-up.\u003c/p\u003e\n\u003ch3\u003eThe Present Study\u003c/h3\u003e\n\u003cp\u003eThis study was designed in response to the widening gap between surgical innovation and educational access in Lebanon. Despite the presence of advanced surgical systems, opportunities to engage meaningfully with laparoscopic and robotic surgery remain limited for the majority of medical trainees. This is not solely a matter of technology, but of systemic strain. The combined impact of the country’s financial collapse, prolonged political instability, the COVID-19 pandemic, and the catastrophic Beirut port explosion has destabilized every pillar of the healthcare system, from infrastructure and staffing to clinical exposure and academic continuity (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e), and this rise in poverty might translate in the increasing difficulty of accessing simulation labs, surgical observership, and faculty mentorship which are critical to developing MIS competency (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). The disconnect between technological availability and trainee access, in a country that historically led regional adoption, denotes the urgency of this inquiry.\u003c/p\u003e \u003cp\u003eThe aim of this study is to fill the major existing gap in the literature about the current state of knowledge, attitudes, and practices related to MIS among Lebanese medical students, and to identify the institutional and personal factors that influence these outcomes. By framing the issue within Lebanon’s complex socioeconomic and academic landscape, this study offers insight into a broader regional challenge: how can surgical education in resource-constrained environments keep pace with global advancements?\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003c/div\u003e \u003c/div\u003e\n\n\n\n "},{"header":"Methods","content":"\u003ch2\u003eStudy Design and Participants\u003c/h2\u003e\u003cp\u003eA cross-sectional design was used to assess the knowledge, attitudes, and practices related to MIS among medical students in Lebanon. The study was conducted over a five-month period from December 2024 to April 2025 and targeted individuals enrolled in recognized Lebanese medical schools.\u003c/p\u003e\u003cp\u003eEligibility criteria included participants from all academic stages, spanning pre-clinical years (MED I \u0026amp; II), clinical years (MED III \u0026amp; IV). The sampling strategy followed a “snowball” approach across medical faculties, to maximize geographic and institutional representation across both public and private universities.\u003c/p\u003e\u003ch3\u003eSample Size Calculation\u003c/h3\u003e\u003cp\u003eThe minimum sample size was calculated using the Epi Info™ StatCalc tool (Centers for Disease Control and Prevention, USA) (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Assuming a total population of approximately 2,800 medical students in Lebanon, with a confidence level of 95%, a margin of error of 5%, and an expected response distribution of 50%, the minimum sample size was estimated at 338 participants. A total of 346 responses were collected, meeting the threshold for statistical representativeness.\u003c/p\u003e\u003ch3\u003eQuestionnaire and Data Collection\u003c/h3\u003e\u003cp\u003eData collection was carried out through an online questionnaire via Google Forms. Prior to participation, digital informed consent was required. If consent was not granted, the form automatically exited without recording any data. Only one participant declined to provide consent and was automatically excluded by the website hosting the questionnaire. The questionnaire was formed using items taken from a published study and the remaining items were developed by the investigating team (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). The questionnaire is available as supplementary file to this paper.\u003c/p\u003e\u003cp\u003eThe questionnaire began with an introductory section detailing the study’s purpose, the research team, and confidentiality assurances, concluding with the consent request. The next section gathered sociodemographic information such as age (via date of birth), gender, year of study, type of medical school (public or private), intended specialty, and participants’ self-rated technological proficiency. Subsequent sections assessed participants' knowledge, attitudes, and practices regarding MIS. Laparoscopic knowledge was evaluated using eleven items covering procedural principles and instrumentation, while robotic surgery knowledge was assessed using thirteen items focusing on system features and clinical indications. A single item asked participants to identify their main sources of MIS knowledge. Practices were evaluated through six items related to prior exposure to training or surgical observation. Finally, a three-item section explored perceived barriers to implementing robotic surgery in Lebanon and participants' views on its curricular integration.\u003c/p\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eData were analyzed using IBM SPSS Statistics version 26.0 and Python 3.11. Descriptive statistics were computed for all variables in the study. Knowledge, attitude, and practice scores were calculated based on predefined coding criteria. The practice score, ranging from 0 to 6, was derived from six binary items and recoded into a binary outcome (low vs. moderate-to-high) for regression analysis. Bivariate analyses included chi-square tests and t-tests to explore associations between KAP scores and demographic variables. Binary logistic regression was then performed to identify independent predictors of moderate-to-high practice. A significance threshold was set at p \u0026lt; 0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive Analysis\u003c/h2\u003e \u003cp\u003eA total of 345 medical students participated in the study. The mean age of respondents was 23.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3 years. Of the total participants, 198 (57.4%) were male and 147 (42.6%) were female. Other sociodemographic factors are represented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\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\u003eSocio-demographic factors of participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal participants, N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e345\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean age, y (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.0 (2.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eGender, N (%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e198 (57.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e147 (42.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTech-savvy respondents, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e183 (53.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eType of medical school, N (%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePublic university\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e150 (43.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrivate university\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e195 (56.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eIntended specialty, N (%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedical specialty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e233 (67.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgical specialty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e112 (32.5)\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\u003eParticipants demonstrated higher average scores in robotic compared to laparoscopic knowledge. When categorized, most participants fell into the moderate knowledge group. Despite generally favorable attitudes toward MIS, reported practice was limited, indicating a gap between disposition and exposure. Details are represented in Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\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 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics of knowledge scores\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScore Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMax Possible Score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMean (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLaparoscopic Knowledge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e60.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRobotic Knowledge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e76.0\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 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of laparoscopic and robotic knowledge levels\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKnowledge Domain\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLevel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLaparoscopic Knowledge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eRobotic Knowledge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.8\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\u003eAttitude scores showed that a majority of participants held positive perceptions of MIS integration in medical training. However, practice scores indicated limited engagement with relevant training or exposure opportunities. Details are represented in Tables\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive summary of attitude and practice scores\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScore Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAttitude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePractice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6\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=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAttitude and practice score levels\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDomain\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLevel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAttitude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e71.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePractice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eBivariate Analysis\u003c/h3\u003e\n\u003cp\u003eChi-square analysis revealed several statistically significant associations. Laparoscopic knowledge was significantly associated with school type (p\u0026thinsp;=\u0026thinsp;0.004), where public university students were more likely to achieve high scores. Robotic knowledge was also associated with school type (p\u0026thinsp;=\u0026thinsp;0.043), with higher scores seen among students from private institutions. Attitude was significantly associated with intended specialty (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and tech-savviness (p\u0026thinsp;=\u0026thinsp;0.027), both linked to more favorable perceptions. Practice score was significantly associated with gender (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), school type (p\u0026thinsp;=\u0026thinsp;0.039), future specialty (p\u0026thinsp;=\u0026thinsp;0.001), and tech-savviness (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Details are represented in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBivariate associations between participant characteristics and high knowledge, attitude, and practice scores \u003cem\u003eNote: Only predictors with statistically significant associations (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) are presented.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDomain\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eχ\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLaparoscopic Knowledge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSchool Type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRobotic Knowledge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSchool Type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAttitude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFuture Specialty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTech-savviness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003ePractice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSchool Type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFuture Specialty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTech-savviness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMultivariable Analysis\u003c/h2\u003e \u003cp\u003eLogistic regression confirmed several independent predictors of high performance. For laparoscopic knowledge, school type remained significant, with students from private institutions less likely to score in the top tertile (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In robotic knowledge, private institution students had greater odds of high performance (p\u0026thinsp;=\u0026thinsp;0.042). High attitude scores were independently predicted by tech-savviness (p\u0026thinsp;=\u0026thinsp;0.001), and high practice scores were significantly predicted by both tech-savviness (p\u0026thinsp;=\u0026thinsp;0.002) and gender (p\u0026thinsp;=\u0026thinsp;0.002), with female students reporting greater engagement. Details are represented in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariable Logistic Regression Identifying Independent Predictors of High Knowledge, Attitude, and Practice Scores \u003cem\u003eNote: Only predictors with statistically significant associations (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) are presented.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDomain\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLaparoscopic Knowledge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSchool Type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.25\u0026ndash;0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRobotic Knowledge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSchool Type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.03\u0026ndash;5.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAttitude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTech-savviness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.81\u0026ndash;10.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePractice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGender (Female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.10\u0026ndash;0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTech-savviness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.90\u0026ndash;16.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eAdditional Analysis\u003c/h2\u003e \u003cp\u003eCluster analysis revealed three respondent profiles. One group showed strong knowledge but low practice; another displayed low scores across all domains; and a third demonstrated high attitude and practice scores despite only moderate knowledge. These clusters reveal variability in learning engagement among participants. Details are represented in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCluster group analysis (K-Means, K\u0026thinsp;=\u0026thinsp;3)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCluster\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLaparoscopic Knowledge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRobotic Knowledge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAttitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePractice\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study aimed to assess the knowledge, attitudes, and practices of medical trainees in Lebanon regarding minimally invasive surgery, and the findings revealed a striking paradox: although students expressed strong enthusiasm and positive attitudes toward MIS, particularly robotic surgery, their practical exposure and foundational knowledge, especially in laparoscopy, remained limited.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eKnowledge\u003c/h2\u003e \u003cp\u003eThe results of this study reveal a moderately strong base of knowledge about minimally invasive surgery among Lebanese medical trainees, particularly regarding robotic techniques. This finding is somewhat surprising, given that laparoscopy has been in widespread surgical practice for a longer history (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e) and is typically more accessible in training settings than robotics (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Nevertheless, a closer look reveals that approximately one-third of students scored in the low knowledge tier for each modality, indicating that significant knowledge gaps persist (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). These findings align with international reports that formal curricula often underrepresent MIS, especially robotics. In a Saudi Arabian study, only 22.6% of students had any formal exposure to robotic surgery, with most relying on informal sources (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe particularly higher robotic knowledge scores may reflect a growing fascination with emerging technology and a tendency among students to engage with digital resources and global surgical trends through online platforms, rather than through hands-on clinical exposure. While this trend suggests that students are proactively seeking information about innovative surgical modalities, it also points to a relative underrepresentation of basic laparoscopic education within current training programs (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). The literature explicitly discusses the importance of introducing fundamental MIS content into undergraduate medical education, including an introduction to operative techniques, instrumentation, and clinical applications (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e), and even brief exposure, such as a 40-minute session, has been shown to boost student confidence in discussing robotic procedures, in a study by Naik and Mandal (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eType of medical school as a predictor\u003c/h2\u003e \u003cp\u003eA notable predictor of MIS knowledge in this study was the type of medical school attended. Institutional affiliation emerged as a significant factor influencing knowledge outcomes, with public university students demonstrating stronger performance in laparoscopic knowledge and private university students showing higher scores in robotic surgery. This distinction reflects differences in curricular focus, and access to technology between institutions. Public universities may focus on traditional operative training, particularly in laparoscopy, as part of a structured general surgery curriculum. In contrast, private institutions, which are often affiliated with better-resourced hospitals, may offer more observational exposure to robotic systems. This finding aligns with findings from an Indian survey that reported none of the government medical colleges had access to robotic platforms in contrast to the private sector (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Similarly, a systematic review of robotic surgery training programs revealed significant disparities in implementation across institutions and called for standardization to ensure equitable training opportunities (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). These findings suggest that institutional context, particularly access to surgical equipment and infrastructure, plays a critical role in shaping both practical experience and theoretical knowledge in MIS.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eAttitude\u003c/h2\u003e \u003cp\u003eAcross the sample, participants expressed generally positive attitudes regarding MIS and its role in their education. Most respondents agreed with the value of acquiring laparoscopic and robotic surgery skills, and a considerable proportion expressed willingness to pursue training opportunities if available with only a minority scoring low on the attitude scores. This reflects broad recognition among future doctors that MIS is an important and worthwhile aspect of modern medicine. Such positive attitudes are consistent with findings from other regions. A survey in Saudi Arabia reported that nearly two-thirds of medical students had a positive outlook on robotic surgery, with many believing it would improve patient outcomes and supporting national investment in robotic services (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Similarly, a recent U.S. study found that 72% of medical students, regardless of intended specialty, supported learning about robotic surgery during undergraduate training, reminding that MIS is a core component of modern clinical competence (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eTech-Savviness as a predictor\u003c/h2\u003e \u003cp\u003eInterestingly, tech-savviness emerged as a significant independent predictor in multivariable analysis. This association suggests the crucial role of digital literacy in shaping medical trainees' perceptions. Trainees comfortable with digital tools are more likely to adopt the sophisticated technologies central to MIS, including robotic-assisted systems and advanced imaging modalities. This observation aligns with evidence showing that digital proficiency enhances both confidence and competence in using innovative surgical techniques (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). In fact, simulation-based and virtual reality training programs have been shown to significantly improve operative performance and cognitive readiness, reinforcing the value of technological fluency in surgical education (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough students expressing interest in surgical careers initially demonstrated more favorable attitudes toward MIS in bivariate analysis, this association did not persist after adjusting for covariates. This attenuation suggests that technological orientation, rather than career interest alone, may drive these attitudes. Supporting this, MacNevin et al. found that students with higher technology readiness were more likely to express interest in both technology-focused and surgical specialties, suggesting a shared inclination toward innovation and technical complexity (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003ePractice\u003c/h2\u003e \u003cp\u003eWhile knowledge and attitudes were generally encouraging, actual practical exposure to MIS among surveyed students was limited. Most students had limited hands-on experience with laparoscopic or robotic equipment, and only a small fraction had participated in simulation-based activities or operating room observations. This discrepancy between enthusiasm and exposure reflects a familiar challenge in surgical education, particularly in low- and middle-income countries (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). For instance, a study in Nigeria by Ijah and Manuel revealed that while medical doctors expressed favorable views toward laparoscopic surgery, most lacked hands-on experience and access to appropriate training resources, which reminds the ongoing disconnect between enthusiasm and opportunity in settings with limited resources (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eTech-Savviness and female gender as predictors\u003c/h2\u003e \u003cp\u003eBivariate analyses identified several demographic and educational factors associated with higher practical exposure to MIS, including gender, school type, future specialty choice, and tech-savviness. However, when controlling for potential confounders in multivariable logistic regression, only gender and tech-savviness were retained as significant independent predictors.\u003c/p\u003e \u003cp\u003eFirst, tech-savvy students were more likely to report higher participation in MIS-related activities. This supports prior research indicating that comfort with digital tools and virtual environments may enhance engagement with simulation-based or technologically advanced training modalities. Indeed, there is evidence that medical trainees who play video games have superior performance on laparoscopic simulators, completing tasks faster and with fewer errors compared to those without gaming experience (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGender differences also emerged, with female students reporting greater practice exposure than their male peers. This finding is surprising and challenges traditional assumptions about gender dynamics in surgical education, likely reflecting increasing proactivity among women pursuing surgical careers. This pattern may reflect a broader response to structural barriers in surgical education. Female students, particularly in contexts with limited opportunities, may be taking deliberate steps to build their experience and visibility in the field. As Bruce et al. suggest, many women in surgical training face gender-based discrimination, necessitating exceeding expectations in order to gain recognition (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). Complementing this, Burgos and Josephson found that gender differences in the teaching and learning of surgery often drive female learners to adopt more intentional and proactive strategies to succeed (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). Thus, this constellation of findings suggests that in constrained training environments, motivated female students may be particularly driven to seek out hands-on experiences, build skills, and increase their presence in the surgical field (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eInterestingly, while intended future specialty and school type were associated with practice scores in bivariate analysis, neither remained significant in the adjusted model. This attenuation suggests that their effects may be mediated by underlying factors such as tech-savviness and possibly individual initiative. This attenuation suggests that their effects may be mediated by underlying factors such as tech-savviness or individual initiative. For example, students attending private institutions may benefit from greater access to robotic platforms and simulation resources due to institutional affiliations with high-tech centers and substantially higher incomes (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). However, it is likely that only those who are technologically inclined or proactive in seeking these opportunities are able to turn access into actual practice (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). Similarly, students planning surgical careers may also self-identify as tech-savvy (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e), engage more readily with digital tools (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e), and actively seek simulation or operating room experiences (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). In both cases, these findings reinforce the idea that individual-level traits may bridge or even outweigh structural differences in training exposure.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eClinical Implications\u003c/h2\u003e \u003cp\u003eThe findings of this study carry significant implications for both curriculum development and policy. First, there is a clear need to introduce MIS concepts and basic skills earlier in the medical education pathway, ideally beginning in the clinical years. Simulation-based training, even with low-cost box trainers or virtual modules, could offer students a safe and standardized environment to build confidence and competence before progressing to the operating room. Institutions with access to high-end robotic systems should make deliberate efforts to democratize exposure, ensuring that access is not limited to select students or elective rotations. Partnerships between universities and national surgical centers could facilitate workshops, boot camps, or certificate programs that increase exposure without placing excessive demands on limited hospital resources.\u003c/p\u003e \u003cp\u003e At a policy level, educational authorities and accreditation bodies should consider establishing national guidelines for minimal MIS exposure by graduation, particularly as these techniques become standard components of clinical care. Moreover, mentorship programs involving MIS-trained faculty could support longitudinal skill development, while helping to further enhance interest in surgical careers and promote gender equity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis study is subject to several limitations. As a cross-sectional survey, it captures attitudes and practices at a single point in time and cannot establish causal relationships. The reliance on self-reported data introduces potential recall and social desirability biases, particularly regarding knowledge accuracy and practice engagement. The snowball sampling approach, although effective in reaching a diverse range of institutions, may have excluded students without access to digital networks or those less engaged with surgical topics, leading to selection bias. Furthermore, variation in robotic exposure is likely influenced by institutional affiliation and may not reflect broader accessibility trends. Despite these limitations, the study provides a valuable snapshot of the current state of MIS preparedness in Lebanon and highlights clear opportunities for improvement in training infrastructure and curriculum design.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study reveals that while Lebanese medical students exhibit positive attitudes toward minimally invasive surgery, major disparities exist in their knowledge and practical exposure, particularly regarding robotic surgery. Factors such as tech-savviness, gender, institutional type, and career orientation influenced KAP outcomes. Despite broad support for integrating MIS into medical education, opportunities for hands-on training remains limited. Hence, there is an urgent need to implement structured, equitable, and simulation-based MIS curricula across all Lebanese medical schools. Doing so will ensure that future physicians are adequately prepared for the evolving landscape of modern surgical care.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received ethical clearance from the Ethics Committee of Notre Dame Des Secours University Hospital (NDS–UH) (Reference: CR1/2025). After presenting the study objectives and investigators, informed consent was obtained from all the participants before proceeding with the questionnaire. Failure to provide consent led to automatic exit from the questionnaire. This study adhered to the Helsinki Declaration for medical research involving human participants.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConsent for publication :\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll authors give their consent for publication.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eClinical trial number: Not applicable\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials: Data is provided within the manuscript or supplementary information files.\u003c/p\u003e\n\u003cp\u003eCompeting interests: None\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFunding declaration: No funding was needed for this study\u003c/p\u003e\n\u003cp\u003eAuthors contributions : A.M wrote the manuscript and created the study design; T.B edited the manuscript; E.G,C.G\u0026amp;J.T contributed in data collection ; J.G supervised the study and reviewed the manuscript before submission\u003c/p\u003e\n\u003cp\u003eAcknowledgements : None\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eStout D, Chaminade T. The evolutionary neuroscience of tool making. Neuropsychologia. 2007;45(5):1091\u0026ndash;100.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArezzo A. The past, the present, and the future of minimally invasive therapy in laparoscopic surgery: a review and speculative outlook. Minim Invasive Ther Allied Technol MITAT Off J Soc Minim Invasive Ther. 2014;23(5):253\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHarrell AG, Heniford BT. Minimally invasive abdominal surgery: lux et veritas past, present, and future. Am J Surg. 2005;190(2):239\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJaffray B. Minimally invasive surgery. Arch Dis Child. 2005;90(5):537\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSoper NJ. Laparoscopic Cholecystectomy. In: Current Review of Minimally Invasive Surgery [Internet]. Springer, York N. NY; 1998 [cited 2025 May 4]. pp. 1\u0026ndash;12. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://link.springer.com/chapter/10.1007/978-1-4612-1692-6_1\u003c/span\u003e\u003cspan address=\"https://link.springer.com/chapter/10.1007/978-1-4612-1692-6_1\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUrbach DR, Stukel TA. Rate of elective cholecystectomy and the incidence of severe gallstone disease. CMAJ Can Med Assoc J. 2005;172(8):1015\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYates DR, Vaessen C, Roupret M. From Leonardo to da Vinci: the history of robot-assisted surgery in urology. BJU Int. 2011;108(11):1708\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHockstein NG, Gourin CG, Faust RA, Terris DJ. A history of robots: from science fiction to surgical robotics. J Robot Surg. 2007;1(2):113\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrassetti A, Ragusa A, Tedesco F, Prata F, Cacciatore L, Iannuzzi A, et al. Robotic Surgery in Urology: History from PROBOT\u0026reg; to HUGOTM. Sensors. 2023;23(16):7104.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeagea T. Laparoscopic cholecystectomy: initial experience in Lebanon. J Med Liban. 1992;40(2):77\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhauli RB, Traboulsi SL, Medawar W, Abu Dargham R, Abdelnoor AM, Hussein MK. Laparoscopic donor nephrectomy: The Middle East experience. Arab J Urol. 2012;10(1):46\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReitano E, Famularo S, Dallemagne B, Mishima K, Perretta S, Riva P, et al. Educational Scoring System in Laparoscopic Cholecystectomy: Is It the Right Time to Standardize? Med Kaunas Lith. 2023;59(3):446.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAli SA, Soomro AG, Mohammad AT, Jarwar M, Siddique AJ. Experience of laparoscopic cholecystectomy during a steep learning curve at a university hospital. J Ayub Med Coll Abbottabad JAMC. 2012;24(1):27\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrian R, Davis G, Park KM, Alseidi A. Evolution of laparoscopic education and the laparoscopic learning curve: a review of the literature. Laparosc Surg. 2022;6:34\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHong M, Rozenblit JW, Hamilton AJ. Simulation-based surgical training systems in laparoscopic surgery: a current review. Virtual Real. 2021;25(2):491\u0026ndash;510.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeyer L, Troyer JD, Mancini J, Bladou F, Berdah SV, Karsenty G. Impact of laparoscopy simulator training on the technical skills of future surgeons in the operating room: a prospective study. Am J Surg. 2011;202(3):265\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeters JH, Fried GM, Swanstrom LL, Soper NJ, Sillin LF, Schirmer B, et al. Development and validation of a comprehensive program of education and assessment of the basic fundamentals of laparoscopic surgery. Surgery. 2004;135(1):21\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShahrezaei A, Sohani M, Taherkhani S, Zarghami SY. The impact of surgical simulation and training technologies on general surgery education. BMC Med Educ. 2024;24(1):1297.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilkinson E, Aruparayil N, Gnanaraj J, Brown J, Jayne D. Barriers to training in laparoscopic surgery in low- and middle-income countries: A systematic review. Trop Doct. 2021;51(3):408\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChao TE, Mandigo M, Opoku-Anane J, Maine R. Systematic review of laparoscopic surgery in low- and middle-income countries: benefits, challenges, and strategies. Surg Endosc. 2016;30(1):1\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSultan I, Bardi MF, Baatta AM, Almaghrabi S, Mohammed RA. Medical Students\u0026rsquo; Attitude Towards Robotic Surgery: A Cross-Sectional Survey. J Med Educ Curric Dev. 2022;9:23821205211066483.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoglia A, Sinceri S, Ferrari V, Ferrari M, Mosca F, Morelli L. Proficiency-based training of medical students using virtual simulators for laparoscopy and robot-assisted surgery: results of a pilot study. Updat Surg. 2018;70(3):401\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNaik R, Mandal I. Robotic simulation experience in undergraduate medical education: a perspective. J Robot Surg. 2020;14(5):793\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBorahay MA, Haver MC, Eastham B, Patel PR, Kilic GS. Modular comparison of laparoscopic and robotic simulation platforms in residency training: a randomized trial. J Minim Invasive Gynecol. 2013;20(6):871\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMullen MG, Salerno EP, Michaels AD, Hedrick TL, Sohn MW, Smith PW, et al. Declining operative experience for junior level residents: Is this an unintended consequence of minimally invasive surgery? J Surg Educ. 2016;73(4):609\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMattar SG, Alseidi AA, Jones DB, Jeyarajah DR, Swanstrom LL, Aye RW, et al. General surgery residency inadequately prepares trainees for fellowship: results of a survey of fellowship program directors. Ann Surg. 2013;258(3):440\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eItani R, Karout L, Kassab MB, Karout S, Rabah M, Mais HEMHE, et al. A crisis amidst many others: COVID-19 response satisfaction during the economic collapse and post-Beirut port explosion in Lebanon: 10.55131/jphd/2024/220102. J Public Health Dev. 2024;22(1):12\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBou Sanayeh E, El Chamieh C. The fragile healthcare system in Lebanon: sounding the alarm about its possible collapse. Health Econ Rev. 2023;13:21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAzadi S, Green IC, Arnold A, Truong M, Potts J, Martino MA. Robotic Surgery: The Impact of Simulation and Other Innovative Platforms on Performance and Training. J Minim Invasive Gynecol. 2021;28(3):490\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEpi Info\u0026trade; | CDC [Internet]. 2025 [cited 2025 May 10]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/epiinfo/index.html\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/epiinfo/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eValle RF, Reichert JA. Laparoscopic Surgery: An Evolving Revolution. JSLS. 2001;5(1):95\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLane T. A short history of robotic surgery. Ann R Coll Surg Engl. 2018;100(6sup):5\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRocha MFH, Coelho RF, Branco AW, Filgueira PH, de O, Guida R. A census of laparoscopic and robotic urological practice: a survey of minimally invasive surgery department of the Brazilian Society of Urology. Int Braz J Urol Off J Braz Soc Urol. 2019;45(4):732\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDoumat G, Daher D, Ghanem NN, Khater B. Knowledge and attitudes of medical students in Lebanon toward artificial intelligence: A national survey study. Front Artif Intell. 2022;5:1015418.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrlando MS, Thomaier L, Abernethy MG, Chen CCG. Retention of laparoscopic and robotic skills among medical students: a randomized controlled trial. Surg Endosc. 2017;31(8):3306\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDarlington D, Anitha FS, Joseph C. A Cross-Sectional Study of Resident Training in Robotic Surgery in India. Cureus 14(2):e22162.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRahimi AO, Ho K, Chang M, Gasper D, Ashouri Y, Dearmon-Moore D, et al. A systematic review of robotic surgery curricula using a contemporary educational framework. Surg Endosc. 2023;37(4):2833\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVigran HJ, Diaz S, Suryadevara A, Blair Y, Aubry S, Reddy RM. Assessing the need for robotic surgery training in standard medical education: insights from medical students. Discov Educ. 2024;3(1):1\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeymour NE, Gallagher AG, Roman SA, O\u0026rsquo;Brien MK, Bansal VK, Andersen DK, et al. Virtual Reality Training Improves Operating Room Performance. Ann Surg. 2002;236(4):458\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAydın A, Ahmed K, Abe T, Raison N, Van Hemelrijck M, Garmo H, et al. Effect of Simulation-based Training on Surgical Proficiency and Patient Outcomes: A Randomised Controlled Clinical and Educational Trial. Eur Urol. 2022;81(4):385\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMacNevin W, Poon E, Skinner TA. Technology readiness of medical students and the association of technology readiness with specialty interest. Can Med Educ J. 2021;12(2):e31\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIjah RFOA, Manuel AD. Knowledge, attitude and practice of laparoscopic surgery among medical doctors in Port Harcourt, Nigeria. Int Surg J. 2020;7(4):970\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOu Y, McGlone ER, Camm CF, Khan OA. Does playing video games improve laparoscopic skills? Int J Surg Lond Engl. 2013;11(5):365\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBruce AN, Battista A, Plankey MW, Johnson LB, Marshall MB. Perceptions of gender-based discrimination during surgical training and practice. Med Educ Online. 2015;20:25923.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurgos CM, Josephson A. Gender differences in the learning and teaching of surgery: a literature review. Int J Med Educ. 2014;5:110\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchlottmann F, Patti MG. Novel simulator for robotic surgery. J Robot Surg. 2017;11(4):463\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRehman S, Raza SJ, Stegemann AP, Zeeck K, Din R, Llewellyn A, et al. Simulation-based robot-assisted surgical training: a health economic evaluation. Int J Surg Lond Engl. 2013;11(9):841\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReimberg J, Lopes LR, Passeri SMRR, Menezes FH. The electronic media and the study profile of the surgical resident. Rev Col Bras Cir. 2021;48:e20212941.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNeumeister MW. Technology and Education: The Future of Plastic Surgery Training. Plast Reconstr Surg Glob Open. 2016;4(6):e777.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCardoso SA, Suyambu J, Iqbal J, Cortes Jaimes DC, Amin A, Sikto JT et al. Exploring the Role of Simulation Training in Improving Surgical Skills Among Residents: A Narrative Review. Cureus 15(9):e44654.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Minimally Invasive Surgery, Laparoscopy, Robotic Surgery, Medical Education, Lebanon, KAP Study, Medical Students","lastPublishedDoi":"10.21203/rs.3.rs-6640818/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6640818/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e\u003c/em\u003e As surgical practice evolves with the rise of minimally invasive techniques, medical education struggles to keep pace, especially in resource-constrained settings. In Lebanon, where advanced systems like robotic platforms are available, access to structured training remains limited.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e\u003c/em\u003e A cross-sectional survey assessed the knowledge, attitudes, and practices related to minimally invasive surgery among 345 Lebanese medical students. Data were collected via an online questionnaire from December 2024 to April 2025. Statistical analysis included descriptive statistics, bivariate tests, and multivariable logistic regression.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003c/em\u003e Participants showed moderate knowledge levels, with higher scores in robotic (76%) than laparoscopic (60.4%) domains. Attitudes toward MIS were overwhelmingly positive (71.3% moderate, 4.9% high), but practical experience was limited (53.9% low practice). Key predictors of high KAP scores included tech-savviness and gender, with female and tech-oriented students showing more engagement. School type also significantly influenced knowledge outcomes: public university students scored higher in laparoscopy, while private university students excelled in robotics.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e\u003c/em\u003e Despite enthusiasm for MIS, practical exposure remains insufficient among Lebanese trainees. This gap indicates the urgent need for equitable, simulation-based training frameworks that can support evolving surgical education in low-resource settings. Early exposure may better prepare future physicians for the evolving landscape of surgical care.\u003c/p\u003e","manuscriptTitle":"Knowledge, attitudes and practices of Lebanese Medical Students towards Minimally invasive surgery in Lebanon : A cross sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-26 11:37:09","doi":"10.21203/rs.3.rs-6640818/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3c4a212f-9995-4a9f-83ec-1d76e6fad9ec","owner":[],"postedDate":"June 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-30T15:53:42+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-26 11:37:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6640818","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6640818","identity":"rs-6640818","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}