Nurses’ behavioral intention in using virtual clinical simulation training: By structural equation modeling

Online learning has been utilized due to the sudden shift taken among educational institutions to continue students’ learning during the COVID-19 pandemic. Three years into the pandemic, universities now offer different modalities of education due to the establishment of online and modular learning modalities. Hence, the intention of students to adapt to online learning despite the availability of traditional learning is underexplored. With the limited availability of face-to-face learning at the near end of the epidemic in the Philippines, this study sought to analyze the factors that influenced behavioral intentions towards continuing online learning modalities. Five hundred students from different universities in the Philippines participated and answered 42 adapted questions in an online survey via Google Forms. Structural equation modeling (SEM) was used in this study, with factors such as an affective latent variable, attitude towards behavior, autonomy, relatedness, competency, expectation, confirmation, satisfaction, and behavioral intention. The study found that attitude towards behavior has the highest positive direct effect on students’ intentions to pursue online learning, followed by expectation and confirmation, satisfaction and behavioral intention, competence and behavioral intention, and the affective variable and satisfaction. The effect of expectations on satisfaction and the affective variable on behavioral intentions was seen to have no significance regarding students’ intentions. This also study integrated expectation–confirmation theory, the theory of planned behavior, and self-determination theory to holistically evaluate students’ intentions to pursue online learning despite the availability of traditional learning. The educational sector can utilize these findings to consider pursuing and offering online learning. Additionally, the study can help future researchers evaluate students’ behavioral intentions concerning online learning.

This thesis evaluates the acceptance of the E-counseling system at the University of Jambi using the Unified Theory of Acceptance and Use of Technology 3 (UTAUT 3) model. This research is based on the problem of mental health levels in students supported by data from WHO and the Ministry of Health. The University of Jambi provides online counseling services to help students solve their problems, but from the pre-research it was found that the level of knowledge and use of E-counseling is still very low. This study aims to implement the UTAUT 3 model which is used to identify factors that influence the acceptance of E-counseling at the University of Jambi. This study took a sample of 100 respondents from all active undergraduate students at the University of Jambi. The analysis technique used in this study is PLS-SEM (Partial Last Structural Equation Modeling) which uses the SmartPLS 4 application in its data calculation. The results obtained from the data calculation show that there are 4 hypotheses that have a significant influence, namely Habit on Use Behavior, Personal Innovativeness on Behavioral Intention, Hedonic Motivation on Behavioral Intention, and Facilitating Conditions on Use Behavior and 8 other hypotheses that do not have a significant influence, namely Performance Expectancy on Behavioral Intention, Effort Expectancy on Behavioral Intention, Social Influence on Behavioral Intention, Facilitating Condition on Behavioral Intention, Price Value on Behavioral Intention, Habit on Behavioral Intention, Personal Innovativeness on Use Behavior and Behavioral Intention on Use Behavior.

The present study examined the relationship among behavioural intention (BI) to adopt online learning, perceived usefulness (PU), perceived ease of use (PEU), self-regulated online learning (SR) and online learning self-efficacy (SE). A total of 900 university students with online learning experience from many provinces of China took part in the study. Structural equation modelling (SEM) was used to analyse the data accepted. The results indicate that PU has a significant positive effect on BI; SR has a significant positive effect on PEU, PU and BI. SE has a significant positive effect on PEU, PU and BI. In addition, SE and SR have significant indirect effects on BI through the mediation of PU. The outcomes have tangible theoretical and practical implications. They not only replicates previous research and provides possible space for further expansion of TAM, but also provide us with an opportunity to reflect on and actively take practical measures to improve BI. These efforts include teachers, parents and other educators trying to promote students’ academic achievements, self-efficacy and self-regulation in the process of online learning. The former is the most concerning issue, while the latter two are the source of students’ motivation. Furthermore, educators should make appropriate use of the role of digital technology in online learning and be careful not to exaggerate the value of digital technology, let alone equate it with online learning.

BackgroundThe implementation of Electronic Health Record (EHR) systems is a critical challenge, particularly in low-income countries, where behavioral intention plays a crucial role. To address this issue, we conducted a study to extend and apply the Unified Theory of Acceptance and Use of Technology 3 (UTAUT3) model in predicting health professionals' behavioral intention to use EHR systems.MethodsA quantitative research approach was employed among 423 health professionals in Southwest Ethiopia. We assessed the validity of the proposed model through measurement and structural model statistics. Analysis was done using SPSS AMOS version 23. Hypotheses were tested using structural equation modeling (SEM) analysis, and mediation and moderation effects were evaluated. The associations between exogenous and endogenous variables were examined using standardized regression coefficients (β), 95% confidence intervals, and p-values, with a significance level of p-value < 0.05.ResultsThe proposed model outperformed previous UTAUT models, explaining 84.5% (squared multiple correlations (R2) = 0.845) of the variance in behavioral intention to use EHR systems. Personal innovativeness (β = 0.215, p-value < 0.018), performance expectancy (β = 0.245, p-value < 0.001), and attitude (β = 0.611, p-value < 0.001) showed significant associations to use EHR systems. Mediation analysis revealed that performance expectancy, hedonic motivation, and technology anxiety had significant indirect effects on behavioral intention. Furthermore, moderation analysis indicated that gender moderated the association between social influence, personal innovativeness, and behavioral intention.ConclusionThe extended UTAUT3 model accurately predicts health professionals' intention to use EHR systems and provides a valuable framework for understanding technology acceptance in healthcare. We recommend that digital health implementers and concerned bodies consider the comprehensive range of direct, indirect, and moderating effects. By addressing personal innovativeness, performance expectancy, attitude, hedonic motivation, technology anxiety, and the gender-specific impact of social influence, interventions can effectively enhance behavioral intention toward EHR systems. It is crucial to design gender-specific interventions that address the differences in social influence and personal innovativeness between males and females.

Changes to Training Practices during a Pandemic - The Experience of the Irish National Trauma & Orthopaedic Training Scheme

The decline in clinical operational skills among recent medical graduates in China presents a pressing challenge, particularly in the context of the “Healthy China 2030” initiative. This study, which is a systematic review using the PRISMA method, systematically examines the contributing factors and potential solutions through a review of contemporary medical education methods, emphasizing the integration of traditional and digital approaches. This study aims to explore factors influencing operational skill acquisition, challenges in clinical training and assessment, and potential solutions identified in prior research, particularly through digital tools such as AI-assisted learning and virtual simulation. This study uses a theoretical framework centered on digitalized medical education, clinical skills training, and assessment methodologies, using content analysis to evaluate and generalize findings from peer-reviewed literature. The results indicate that while digital tools, such as simulation-based training and online platforms, improve student engagement and satisfaction, their impact on operational proficiency remains inconsistent. Traditional methods continue to dominate clinical training, underscoring the need for a blended approach that takes advantage of the strengths of both modalities. The findings also reveal systemic problems in China’s healthcare education system, including insufficient clinical rotation opportunities, lack of standardized assessment frameworks, and socioecological factors such as policy constraints, teacher-student ratios, and economic pressures. This study identifies significant gaps in existing research, particularly the absence of a unified framework to evaluate clinical skills and the limited focus on the digital competencies of undergraduate students. Additionally, demographic factors, including gender and socioeconomic status, emerge as critical influences on skill development. Further analysis suggests that future studies should prioritize longitudinal evaluations of digital education’s long-term impact on clinical practice, especially its application in actual clinical situations.Supplementary InformationThe online version contains supplementary material available at 10.1007/s40670-025-02407-7.

Massive Open Online Courses (MOOCs) are emerging as the new trend for modern higher education institutions. Student acceptance is viewed as the key determinant for the success of MOOCs. This study intends to examine factors influencing higher education students’ behavioral intention to adopt MOOCs. Thus, this study proposes the use of a modified Technology Acceptance Model (TAM). Data is collected via an online survey from a sample of 403 participants in Jordan. Structural equation modeling (SEM) is used to assess the accuracy of the research model. The results reveal that 1) students’ behavioral intention to adopt MOOCs is positively affected by the perceived ease of use and by the perceived usefulness, 2) self-regulated learning has both a negative direct and indirect (through perceived usefulness) influence on behavioral intention, 3) computer self-efficacy and perceived convenience have positive indirect effects on behavioral intention through the perceived usefulness and perceived ease of use, and 4) learning tradition has a negative indirect effect on behavioral intention through self-regulated learning. Based on the results, various implications (both practical and theoretical), and suggestions for future research, have been highlighted.

BackgroundUnbalanced distribution of medical resources is becoming a major challenge, particularly in the selection of doctors. e-Consultation could provide patients with more choices of doctors and break the constraints of time and space. However, the acceptance of e-consultation is still poor and the mechanism of adoption is unclear.ObjectiveThe aim of this study was to identify the factors influencing the public intention to use e-consultation and explore the effect path of the factors and behavior intention.MethodsThe hypotheses of our research model were developed based on the technology acceptance model and perceived risk theory. A web-based survey was conducted by an electronic questionnaire collection platform; this survey that consisted of a 29-item questionnaire with 5-point Likert scales was completed by 934 respondents. Structural equation modeling was used to analyze the data. Item evaluation and reliability, validity, path loading, goodness of fit, and multiple group analysis were used to check the moderation effects.ResultsThe standardized factor loadings of the items were between 0.551 and 0.873. The composite reliability of 9 constructs ranged from 0.706 to 0.840. The average variance extracted ranged from 0.387 to 0.640. The fitness indices showed that the collected data fitted well with the research model. Perceived usefulness was the strongest positive factor effecting behavior intention (β=.399, P<.001). Perceived ease of use had a positive effect on behavior intention but it was not statistically significant (β=.117, P=.07) and it had a positive effect on perceived usefulness (β=.537, P<.001). Perceived risk could be well explained by financial risk (β=.972, P<.001), privacy risk (β=.774, P<.001), social risk (β=.871, P<.001), time risk (β=.894, P<0.001), and psychological risk (β=.774, P<.001). Perceived risk had negative effects on perceived usefulness (β=–.375, P<.001) and behavior intention (β=–.297, P<.001). Personal innovativeness had a positive influence on perceived ease of use (β=.241, P<.001) and a slight effect on behavior intention (β=.124, P=.001). Age (χ258=133.5, P<.001) and usage experience (χ258=82.5, P=.02) had a slight moderation effect on the paths.ConclusionsPerceived usefulness and perceived risk have significant effects on public intention to use e-consultation. Therefore, platform and manufacturer must improve the function of e-consultation, which will promote the public intention to use e-consultation fundamentally. In order to control the perceived risk of public, government should play an important role in enforcing management of e-consultation markets and approving corresponding medical insurance policies. Besides, personal innovativeness had an effect on behavior intention. Moreover, the paths of factors had some heterogeneity among people with different characteristics. Therefore, it is necessary to adjust the strategies to fit more groups better.

This study examined the attitudes toward online learning of EFL students by applying technology acceptance model (TAM) constructs along with the characteristics of the online learning system and the experience of online learning as external independent variables. The study used a quantitative approach. The researchers collected data by administering a questionnaire using Microsoft’s online form to the tertiary students who were studying for their Bachelor of Arts degree (B.A.) in the English Language and Literature Department at Al-Balqa Applied University – Jordan. . One hundred eighty-five students out of 602 responded to the questionnaire. The researchers used SPSS and Structural Equational Modeling (SEM – Smart PLS) to analyze the data. Among the findings, the characteristics of an online learning system significantly affected perceived usefulness (PU), perceived ease of use (PEOU), attitudes, and the behavioral intentions (BIs) of EFL learners. In contrast, EFL learners’ PEOU negatively affected their attitudes towards online learning and BIs. Finally, the attitudes of students who studied online for four semesters or more were negative compared to those who took less than four semesters online. These findings could be attributed to the supposedly temporary and rapid shift to online learning due to Covid-19 lockdown measures. This study is significant because it develops an understanding of tertiary EFL attitudes towards online learning. It also reflects on their behavioral intentions to utilize online learning after the pandemic. The researchers believe that this is the first study to examine EFL learners’ attitudes and BIs on using online learning during/after the pandemic in Jordan using TAM.

Many seem to believe that today’s pre-service teachers as born after 1980 are digital natives, or that they are “native speakers” of the digital language. Nevertheless, there is no evidence that their digital native characteristics determine whether or not they would use technology in the classroom. Although not widely evaluated, the four-factor, 21-item Digital Nativity Assessment Scale (DNAS) was one of the first instruments to assess digital nativeness (DN). This study aim is to explore the influence of pre-service teachers’ DN on their intention to use technology in the future classroom in Serbia, by evaluating the DNAS on Serbian sample and using it for measuring the DN. Six variables were incorporated to examine their mutual relationships based on technology acceptance model: digital nativeness, behavioral intention (BI), perceived usefulness (PU), perceived ease of use (PEU), subjective norm (SN), and technological complexity (TC). Data were collected from 688 pre-service teachers in Serbia. Exploratory factor analysis confirmed a four-factor model for the DNAS, and Serbian pre-service teachers demonstrated a high level of DN. To evaluate the hypothesized model structural equation modeling was utilized. The suggested model had a good fit for describing the BI of Serbian pre-service teachers, accounting for 27.1% of the variance in BI. It was found that direct dominant predictors of BI are digital native traits, perceived usefulness, and perceived ease of use. Significant influence of digital native traits on all other variables in the model was also confirmed. The implications for theory and practice are discussed.

Online learning versus blended learning of clinical supervisee skills with pre-registration nursing students: A randomised controlled trial

Clinical Virtual Simulation as Lifelong Learning Strategy—Nurse's Verdict

Across the world, Higher Education Institutions (HEIs) were mandatory to transform their teaching model from face-to-face interaction to online learning. Neither the faculty nor the students were ready for this sudden transformation, most of the HEIs were practicing online learning for the sake of changing or technology adoption. COVID-19 changes the HEIs standpoint towards online delivery, as of mid of March 2020, the only online learning come to be the reasonable solution for teaching in schools and HEIs. Therefore, this study aimed to develop a research model to explore the effect of perceived enjoinment, perceived ease of use, perceived usefulness, social influence on the students’ intention and behavioral towards online learning during COVID-19 in Oman. The developed model was validated by employing Structural Equation Modeling (SEM) and the data collected via online questionnaire from 191 students whom have just completed the “Educational Technology” course on Spring 2020 at A’Sharqiyah University-Oman (ASU). The main finding of this study indicating that the identified factors; PEOU and SI significantly influenced students’ Behavioral Intention (BI) towards online learning, where others; PE and PU were not supported in this study.KeywordsHigher Education InstitutionsCovid-19 pandemicOnline learning

Virtual Simulation of Intraoperative Decision-Making for Open Abdominal Aortic Aneurysm Repair: A Mixed Methods Analysis

The purpose of this study was to establish a structural model between technology anxiety, SSTQUAL, customer satisfaction and behaviour intention toward SST and verify the multiple mediating effects of SSTQUAL and customer satisfaction. Among 300 questionnaires distributed, 282 effective responses were entered for data analysis. The collected data were analyzed through reliability test, correlation analysis and structural equation modeling(SEM). In addition, a phantom model was constructed and the significance of the multiple mediating effect was verified by using a bootstrapping method. The findings of the study are as follows. First, technology anxiety found to have a negative effect on the SSTQUAL and behaviour intention, but, not to have a significant effect on satisfaction. Second, SSTQUAL was found to have a positive effect on satisfaction and behaviour intention. Third, customer satisfaction was found to have a positive effect on behaviour intention. Finally, the mediating effects of SSTQUAL was significant in the relationship between technology anxiety and behaviour intention. Also, there was significant statistical multiple mediating effects on SSTQUAL and satisfaction in the relationship between technology anxiety and behaviour intention toward SST.