Interprofessional collaboration between different health care professions in emilia romagna

Background and aim of the work: Interprofessional collaboration in the healthcare sector contributes to the delivery of high quality and safe services to patients across different subdivisions of the healthcare system which is faced with constant challenges. The international literature offers a plethora of tools for assessing the collaboration between health workers, but only a few of these have been validated in the Italian language. One that has undergone such validation is the interprofessional collaboration (IPC) scale, which measures the perception of collaboration among health professionals. An advantage of this scale is that is addresses all workers within the system, and is not limited to specific professions. The aim of the present study was to apply the validated Italian version of the IPC scale, to a context different to the one used for its validation, to measure the level of collaboration between different health care workers. Method: A questionnaire-based study was conducted on a sample consisting of 329 health professionals working at Azienda USL-IRCCS in Reggio Emilia. The categorical and continuous variables were analysed using descriptive statistics (frequen-cies, percentages and standard deviations). Results: The IPC scale showed physicians to express the highest level of collaboration with other professionals, in line with the results of other studies in the literature. The values calculated for the factors “accommodation” and “communication” were higher than for “isolation”, de-picting a good level collaboration. The only case in which the isolation factor, which describes an absence of collaboration, was equal to the other two factors was in relation to the evaluation of midwives by nursing aides/orderlies. Conclusions: In conclusion, the Italian version of the IPC scale provides a useful instrument for measuring interprofessional collaboration between workers in the healthcare sector. In the present study, it revealed a satisfactory level of collaboration between health professionals in an organization located in Emilia Romagna, Italy. (www.actabiomedica.it)

Storia dell'Italia Unita

Si tratta di un'ampia e articolata sintesi della storia nazionale dal 1861 a oggi, suddivisa in 5 parti - la politica estera e l'Italia nel mondo, la storia politica, lo sviluppo economico, le trasformazioni sociali, gli intelletuali e la cultura - sostenuta da una forte tensione interpretativa e da un un solido impianto metodologico. Luigi Ganapini ha scritto le prime due parti (pp. 9-412), Alberto De Bernardi le altre tre (pp. 415-1088)

Epistemic Planning in a Fast and Slow Setting

AI applications are by now pervading our everyday life. Nonetheless, most of these systems lack many capabilities that, we humans, naturally consider to be included in a notion of “intelligence”. In this paper we present a multi-agent system, inspired by the cognitive theory known as thinking fast and slow by D. Kahneman, to solve Multi-agent Epistemic Planning (MEP) problems. This is an instance of a general AI architecture, referred to as SOFAI (for Slow and Fast AI). This paradigm exploits multiple solving approaches (referred to as fast and slow solvers) and a metacognition module to arbitrate between them and enhance the reasoning process, that, in this specific case, is concerned with planning in epistemic settings. The behavior of this system is then compared to a state-of-the-art MEP solver, showing that the newly introduced system presents better results in terms of generality, solving a much wider set of problems with an acceptable trade-off between solving times and solution accuracy

Value-based Fast and Slow AI Nudging

Nudging is a behavioral strategy aimed at influencing people's thoughts and actions. Nudging techniques can be found in many situations in our daily lives, and these nudging techniques can targeted at human fast and unconscious thinking, e.g., by using images to generate fear or the more careful and effortful slow thinking, e.g., by releasing information that makes us reflect on our choices. In this paper, we propose and discuss a value-based AI-human collaborative framework where AI systems nudge humans by proposing decision recommendations. Three different nudging modalities, based on when recommendations are presented to the human, are intended to stimulate human fast thinking, slow thinking, or meta-cognition. Values that are relevant to a specific decision scenario are used to decide when and how to use each of these nudging modalities. Examples of values are decision quality, speed, human upskilling and learning, human agency, and privacy. Several values can be present at the same time, and their priorities can vary over time. The framework treats values as parameters to be instantiated in a specific decision environment

Combining Fast and Slow Thinking for Human-like and Efficient Navigation in Constrained Environments

Current AI systems lack several important human capabilities, such as adaptability, generalizability, self-control, consistency, common sense, and causal reasoning. We believe that existing cognitive theories of human decision making, such as the thinking fast and slow theory, can provide insights on how to advance AI systems towards some of these capabilities. In this paper, we propose a general architecture that is based on fast/slow solvers and a metacognitive component. We then present experimental results on the behavior of an instance of this architecture, for AI systems that make decisions about navigating in a constrained environment. We show how combining the fast and slow decision modalities, which can be implemented by learning and reasoning components respectively, allows the system to evolve over time and gradually pass from slow to fast thinking with enough experience, and that this greatly helps in decision quality, resource consumption, and efficiency

Combining Fast and Slow Thinking for Human-like and Efficient Decisions in Constrained Environments

Current AI systems lack several important human capabilities, such as adaptability, generalizability, self-control, consistency, common sense, and causal reasoning. We believe that existing cognitive theories of human decision making, such as the thinking fast and slow theory, can provide insights on how to advance AI systems towards some of these capabilities. In this paper, we propose a general architecture that is based on fast/slow solvers and a metacognitive component. We then present experimental results on the behavior of an instance of this architecture, for AI systems that make decisions about navigating in a constrained environment. We show how combining the fast and slow decision modalities, which can be implemented by learning and reasoning components respectively, allows the system to evolve over time and gradually pass from slow to fast thinking with enough experience, and that this greatly helps in decision quality, resource consumption, and efficiency

Thinking Fast and Slow in AI: The Role of Metacognition

Artificial intelligence (AI) still lacks human capabilities, like adaptability, generalizability, self-control, consistency, common sense, and causal reasoning. Humans achieve some of these capabilities by carefully combining their thinking “fast” and “slow”. In this work we define an AI architecture that embeds these two modalities, and we study the role of a “meta-cognitive” component, with the role of coordinating and combining them, in achieving higher quality decisions