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High-fidelity simulation (Hi-Fi SIM) is increasingly used to provide undergraduate interprofessional education (IPE). Although research has reported positive student feedback, studies have predominantly involved medical and nursing specialties. The present study sought to further explore this area by determining the perceptions of medical, physiotherapy and nursing students participating in the same simulation session. A total of 145 medical, physiotherapy and nursing undergraduate students jointly participated in a novel Hi-Fi SIM IPE programme. Immediately before and after their session, students completed the KidSIM ATTITUDES questionnaire where statements were rated regarding simulation, IPE and human factors. A high score indicated a more positive attitude. Physiotherapy students reported the lowest level of previous Hi-Fi SIM experience. Students from each specialty had more positive attitudes related to simulation, IPE and human factors following their simulation. Physiotherapy students had predominantly less positive attitudes compared with nursing and medical students. Participation in an IPE Hi-Fi SIM session positively impacted on the perceptions of medical, physiotherapy and nursing students regarding the relevance of simulation, IPE and the importance of human factors. Such findings support the use of this learning modality for the provision of IPE in a range of specialties.

The safety of novel therapeutics and vaccines are typically assessed in early phase clinical trials involving “healthy volunteers.” Abnormalities in such individuals can be difficult to interpret and may indicate previously unrecognized medical conditions. The frequency of incidental findings (IFs) in healthy volunteers who attend for clinical trial screening is unclear. To assess this, we retrospectively analyzed data for 1838 “healthy volunteers” screened for enrolment in a UK multicenter, phase I/II severe acute respiratory syndrome‐coronavirus 2 (SARS‐COV‐2) vaccine trial. Participants were predominantly White (89.7%, 1640/1828) with a median age of 34 years (interquartile range [IQR] = 27–44). There were 27.7% of participants (510/1838) who had at least one IF detected. The likelihood of identifying evidence of a potential, new blood‐borne virus infection was low (1 in 238 participants) compared with identification of an elevated alanine transaminase (ALT; 1 in 17 participants). A large proportion of participants described social habits that could impact negatively on their health; 21% consumed alcohol in excess, 10% were current smokers, 11% described recreational drug use, and only 48% had body weight in the ideal range. Our data demonstrate that screening prior to enrollment in early phase clinical trials identifies a range of IFs, which should inform discussion during the consent process. Greater clarity is needed to ensure an appropriate balance is struck between early identification of medical problems and avoidance of exclusion of volunteers due to spurious or physiological abnormalities. Debate should inform the role of the trial physician in highlighting and advising about unhealthy social habits.

The use of the Cloud computing has been constantly on the rise. The flexible billing model coupled with elastic resource provisioning make the Cloud appealing to consumers. However there are still many challenges associated with the Cloud limiting its adoption, such as vendor lock-in and security concerns. One way to address some of these challenges is to use services from more than one Cloud providers. This may help in avoiding the case of vendor lock-in and will also allow for the use of multiple resources available at multiple Clouds. The use of multi-cloud environments can also assist in the case of Cloud bursting where a workload in a private cloud bursts into a public cloud when the need arises. However, the security concerns in such an environment are amplified when compared to a single Cloud. In this paper we address the specification and consistency management of authorization policies in Multi-Cloud environments. The problem being address is significant as an erroneous authorization policy can have severe consequences on the security of the system being protected. In a Multi-Cloud environment, it is difficult to ensure consistency with different Clouds having authorization models, different implementations of the same authorization model and different access control policies. To this end, we have proposed a formal Event-Calculus based model to model the aggregated authorization policies from multiple Cloud providers. The translated Event-Calculus models are then reasoned upon to identify the policy conflicts. We have applied our approach on authorization policies from AWS, GCP and Microsoft Azure. Further, we have provided tool support to automate the complete verification process and provided detailed performance evaluation results to justify the practicality and scalability of the proposed approach.