Background: The aim of this study is to validate a newly developed nurses' self-efficacy sources inventory. We test the validity of a five-dimensional model of sources of self-efficacy, which we contrast with the traditional four-dimensional model based on Bandura's theoretical concepts. Methods: Confirmatory factor analysis was used in the development of the newly developed self-efficacy measure. Model fit was evaluated based upon commonly recommended goodness-of-fit indices, including the χ2 of the model fit, the Root Mean Square Error of approximation (RMSEA), the Tucker-Lewis Index (TLI), the Standardized Root Mean Square Residual (SRMR), and the Bayesian Information Criterion (BIC). Results: All 22 items of the newly developed five-factor sources of self-efficacy have high factor loadings (range .40-.80). Structural equation modeling showed that a five-factor model is favoured over the four-factor model. Conclusions and implications: Results of this study show that differentiation of the vicarious experience source into a peer- and expert based source reflects better how nursing students develop self-efficacy beliefs. This has implications for clinical learning environments: a better and differentiated use of self-efficacy sources can stimulate the professional development of nursing students.
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The After-Action Review (AAR) in Virtual Reality (VR) training for police provides new opportunities to enhance learning. We investigated whether perspectives (bird’s eye & police officer, bird’s eye & suspect, bird’s eye) and line of fire displayed in the AAR impacted the officers’ learning efficacy. A 3 x 2 ANOVA revealed a significant main effect of AAR perspectives. Post hoc pairwise comparisons showed that using a bird’s eye view in combination with the suspect perspective elicits significantly greater learning efficacy compared to using a bird’s eye view alone. Using the line of fire feature did not influence learning efficacy. Our findings show that the use of the suspect perspective during the AAR in VR training can support the learning efficacy of police officers.Practitioner summary: VR systems possess After-Action Review tools that provide objective performance feedback. This study found that reviewing a VR police training scenario from the bird’s eye view in combination with the suspect perspective enhanced police officers’ learning efficacy. Designing and applying the After-Action Review effectively can improve learning efficacy in VR.
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The authors present the study design and main findings of a quasi-experimental evaluation of the learning efficacy of the Serious Game (SG) 'Hazard Recognition' (HR). The SG-HR is a playable, two-level demonstration version for training supervisors who work at oil and gas drilling sites. The game has been developed with a view to developing a full-blown, game-based training environment for operational safety in the oil and gas industry. One of the many barriers to upscaling and implementing a game for training is the questioned learning efficacy of the game. The authors therefore conducted a study into the game's learning efficacy and the factors that contribute to it. The authors used a Framework for Comparative Evaluation (FCE) of SG, and combined it with the Kowalski model for Hazard Detection and the Noel Burch competence model. Four experimental game sessions were held, two involving 60 professionals working in the oil and gas industry, and two with engineering students and consultants. Relevant constructs were operationalized and data were gathered using pre and post-game questionnaires. The authors conclude that the SG-HR improves players' skills and knowledge on hazard detection and assessment, and it facilitates significant learning efficacy in this topic. The FCE proved very helpful for setting up the evaluation and selecting the constructs.
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Inhalation therapy is essential for the management of respiratory conditions such as asthma and chronic obstructive pulmonary disease. However, current inhalation systems face limitations, including polydisperse aerosols that reduce drug delivery efficiency and complex treatment regimens that affect patient adherence. To improve drug targeting and efficacy, Gilbert Innovation B.V. is developing a next-generation soft-mist inhaler based on electrohydrodynamic atomization (EHDA), which produces uniform micrometer sized droplets. Effective drug delivery requires high flow rates and precise aerosol discharge to ensure deep lung deposition while minimizing losses to the device and oropharynx. To achieve this, the device employs a multi-nozzle system for increased flow and corona discharge needles for charge neutralization. However, ensuring uniform neutralization across multiple nozzles and maintaining stable electrospray operation remain key challenges. COSMIC aims to increase system robustness by optimizing neutralization efficiency, refining material selection, and controlling electrospray stability under varying conditions. The electrospray control system will incorporate advanced strategies leveraging computer vision, machine learning and big data analytics. These innovations will increase efficiency, accessibility and patient comfort in inhalation therapy.
