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Background: Using virtual patients integrated in simulators expands students' training opportunities in healthcare. However, little is known about the usability perceived by students and the factors/determinants that predict the acceptance and use of clinical virtual simulation in nursing education.
Objectives: To identify the factors/determinants that predict the acceptance and use of clinical virtual simulation in learning in nursing education.
Methods: Observational, cross-sectional, analytical study of the use of clinical virtual simulation in nursing to answer the research question: What factors/determinants predict the acceptance and use of a clinical virtual simulator in nursing education? We used a non-probabilistic sampling, more specifically a convenience sample of nursing degree students. The data were collected through a questionnaire adapted from the Technology Acceptance Model 3. In technology and education, the Technology Acceptance Model is a theoretical model that predicts the acceptance of the use of technology by users.
Results: The sample comprised 619 nursing students, who revealed mean values of perceived usefulness (M = 5.34; SD = 1.19), ease of use (M = 4.74; SD = 1.07), and intention to use the CVS (M = 5.21; SD = 1.18), in a Likert scale of seven points (1-the worst and 7 the best possible opinion). This study validated the use of Technology Acceptance Model 3 adapted and tested the related hypotheses, showing that the model explains 62% of perceived utility, 32% of ease of use, and 54% of intention to use the clinical virtual simulation in nursing by nursing students. The adequacy of the model was tested by analysis of the direct effects of the relationships between the internal constructs (PU-BI, β = 0.11, p = 0.012; PEOU-BI, β = -0.11, p = 0.002) and the direct relations between some of the constructs internal to the Technology Acceptance Model 3 and the external determinants Relevance for learning and Enjoyability. In the proposed model, the external constructs that best predicted perceived usefulness, ease of use, and behaviour intention to use the clinical virtual simulation in nursing were Relevance for learning and Enjoyability.
Conclusions: These study results allowed us to identify relevance for learning and enjoyability as the main factors/determinants that predict the acceptance and use of clinical virtual simulation in learning in nursing.
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http://dx.doi.org/10.1186/s12909-024-05154-2 | DOI Listing |
J Telemed Telecare
September 2025
Centre for Innovative Medical Technology, Odense University Hospital, Odense, Denmark.
IntroductionThe use of digital solutions including patient-reported outcomes is limited to follow-up of patients with established diagnoses but is rarely used as first step of the diagnostic process substituting a personal contact with a health professional. We report on the diagnostic validity and cost per patient implications based on a feasibility study of a new virtual diagnostic service (VDS) for common neurological sleep disorders that, as a first step, involves the collection and automated analysis of self-reported digital patient data.MethodsThe VDS was established at the Odense University Hospital, Denmark.
View Article and Find Full Text PDFAdv Ther
September 2025
Teva Branded Pharmaceutical Products R&D LLC, West Chester, PA, USA.
Introduction: Pharmacokinetic differences between long-acting injectable antipsychotic (LAI) formulations, combined with a lack of clinical switch studies, contribute to clinician uncertainty when transitioning between LAIs. This analysis employed a population pharmacokinetic (popPK) modeling approach to characterize dosing conversions and switching strategies from intramuscular paliperidone palmitate once monthly (PP1m) to TV-46000, a long-acting subcutaneous formulation of risperidone, once monthly (q1m), with a secondary analysis of PP1m to TV-46000 every 2 months (q2m).
Methods: For PP1m and TV-46000, concentration-time profiles for paliperidone and TV-46000 total active moiety (TAM; risperidone + paliperidone) were simulated on the basis of published popPK models with virtual populations of 5000 patients.
Interv Neuroradiol
September 2025
University Clinic for Neuroradiology, University Hospital Magdeburg, Magdeburg, Germany.
BackgroundAt present, nonvirtual neurovascular training can be performed using either an angiographic suite under fluoroscopic guidance (entailing radiation exposure) or direct optical visualization with a camera-based system. The angiographic approach offers high-fidelity visualization and catheter control but is constrained by the limited availability of such specialized facilities, whereas the camera-based approach can be implemented virtually anywhere yet lacks comparable realism in key procedural aspects. The objective of this work is to develop and evaluate a novel camera-based angiography training system (CBATS) that generates artificial angiograms and roadmaps, thereby combining the advantages of both imaging techniques while eliminating radiation exposure.
View Article and Find Full Text PDFJ Am Coll Surg
September 2025
Department of Surgery, Division of Cardiothoracic Surgery, University of Colorado, Aurora, CO.
Background: Gender disparities exist in cardiothoracic surgery (CT), though qualitative investigations are lacking. We aimed to explore the impact of workplace culture on belonging, burnout, and career exit for women in CT.
Study Design: We conducted virtual semi-structured interviews with women cardiothoracic surgeons in practice for ≥5 years across the United States from 9/2024 to 12/2024.
Clin Implant Dent Relat Res
October 2025
State Key Laboratory of Oral Diseases, National Center for Stomatology, National Clinical Research Center for Oral Diseases, Department of Oral Implantology, West China Hospital of Stomatology, Sichuan University, Chengdu, China.
Introduction: This in vitro study evaluated how different forces applied to the dental drill handle during static computer-assisted implant surgery influence surgical guide deformation and implant placement accuracy.
Methods: Twenty-four virtual implants were divided into six groups (0-10 N, in 2 N increments). Surgical guides were scanned under loaded conditions, and deviations were quantified by superimposition with the baseline model.