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Background: Nursing education consists of theory and practice, and student nurses’ perception of the learning environment, both educational and clinical, is one of the elements that determines the success or failure of their university study path. This study aimed to identify the currently available tools for measuring the clinical and educational learning environments of student nurses and to evaluate their measurement properties in order to provide solid evidence for researchers, educators, and clinical tutors to use in the selection of tools. Methods: We conducted a systematic review to evaluate the psychometric properties of self-reported learning environment tools in accordance with the Consensus-based Standards for the Selection of Health Measurement Instruments (COSMIN) Guidelines of 2018. The research was conducted on the following databases: PubMed, CINAHL, APA PsycInfo, and ERIC. Results: In the literature, 14 instruments were found that evaluate both the traditional and simulated clinical learning environments and the educational learning environments of student nurses. These tools can be ideally divided into first-generation tools developed from different learning theories and second-generation tools developed by mixing, reviewing, and integrating different already-validated tools. Conclusion: Not all the relevant psychometric properties of the instruments were evaluated, and the methodological approaches used were often doubtful or inadequate, thus threatening the instruments’ external validity. Further research is needed to complete the validation processes undertaken for both new and already developed instruments, using higher-quality methods and evaluating all psychometric properties

In this paper, we consider a general challenging distributed optimization setup arising in several important network control applications. Agents of a network want to minimize the sum of local cost functions, each one depending on a local variable, subject to local and coupling constraints, with the latter involving all the decision variables. We propose a novel fully distributed algorithm based on a relaxation of the primal problem and an elegant exploration of duality theory. Despite its complex derivation, based on several duality steps, the distributed algorithm has a very simple and intuitive structure. That is, each node finds a primal-dual optimal solution pair of a local relaxed version of the original problem and then updates suitable auxiliary local variables. We prove that agents asymptotically compute their portion of an optimal (feasible) solution of the original problem. This primal recovery property is obtained without any averaging mechanism typically used in dual decomposition methods. To corroborate the theoretical results, we show how the methodology applies to an instance of a distributed model-predictive control scheme in a microgrid control scenario.

Background: Standard precautions (SPs) are first-line strategies with a dual goal: to protect health care workers from occupational contamination while providing care to infected patients and to prevent/reduce health care-associated infections (HAIs). This study aimed at (1) identifying the instruments currently available for measuring healthcare professionals’ compliance with standard precautions; (2) evaluating their measurement properties; and (3) providing sound evidence for instrument selection for use by researchers, teachers, staff trainers, and clinical tutors. Methods: We carried out a systematic review to examine the psychometric properties of standard precautions self-assessment instruments in conformity with the COSMIN guidelines. The search was conducted on the databases PubMed, CINAHL, and APA PsycInfo. Results: Thirteen instruments were identified. These were classified into four categories of tools assessing: compliance with universal precautions, adherence to standard precautions, compliance with hand hygiene, and adherence to transmission-based guidelines and precautions. The psychometric properties of instruments and methodological approaches of the included studies were often not satisfactory. Only four instruments were classified as high-quality measurements. Conclusions: The available instruments that measure healthcare professionals’ compliance with standard precautions are of low-moderate quality. It is necessary that future research completes the validation processes undertaken for long-established and newly developed instruments, using higher-quality methods and estimating all psychometric properties.

In this paper we introduce disropt, a Python package for distributed optimization over networks. We focus on cooperative set-ups in which an optimization problem must be solved by peer-to-peer processors (without central coordinators) that have access only to partial knowledge of the entire problem. To reflect this, agents in disropt are modeled as entities that are initialized with their local knowledge of the problem. Agents then run local routines and communicate with each other to solve the global optimization problem. A simple syntax has been designed to allow for an easy modeling of the problems. The package comes with many distributed optimization algorithms that are already embedded. Moreover, the package provides full-fledged functionalities for communication and local computation, which can be used to design and implement new algorithms. disropt is available at github.com/disropt/disropt under the GPL license, with a complete documentation and many examples.

We present a distributed optimization algorithm for solving online personalized optimization problems over a network of computing and communicating nodes, each of which linked to a specific user. The local objective functions are assumed to have a composite structure and to consist of a known time-varying (engineering) part and an unknown (user-specific) part. Regarding the unknown part, it is assumed to have a known parametric (e.g., quadratic) structure a priori, whose parameters are to be learned along with the evolution of the algorithm. The algorithm is composed of two intertwined components: (i) a dynamic gradient tracking scheme for finding local solution estimates and (ii) a recursive least squares scheme for estimating the unknown parameters via user's noisy feedback on the local solution estimates. The algorithm is shown to exhibit a bounded regret under suitable assumptions. Finally, a numerical example corroborates the theoretical analysis.

In this paper we propose a new control-oriented design technique to enhance the algorithmic performance of the distributed gradient tracking algorithm. We focus on a scenario in which agents in a network aim to cooperatively minimize the sum of convex, quadratic cost functions depending on a common decision variable. By leveraging a recent system-theoretical reinterpretation of the considered algorithmic framework as a closed-loop linear dynamical system, the proposed approach generalizes the diagonal gain structure associated to the existing gradient tracking algorithms. Specifically, we look for closed-loop gain matrices that satisfy the sparsity constraints imposed by the network topology, without however being necessarily diagonal, as in existing gradient tracking schemes. We propose a novel procedure to compute stabilizing sparse gain matrices by solving a set of nonlinear matrix inequalities, based on the solution of a sequence of approximate linear versions of such inequalities. Numerical simulations are presented showing the enhanced performance of the proposed design compared to existing gradient tracking algorithms.