Morale of mental health professionals in Community Mental Health Services of a Northern Italian Province.

Publisher version: http://journals.cambridge.org/action/displayJournal?jid=EPSAIMS: To explore morale of psychiatrists and psychiatric nurses working in Community Mental Health Centres (CMHC) in an Italian Province, and identify influential factors. METHODS: Thirty psychiatrists and 30 nurses working in CMHCs in Modena completed questionnaires on burnout, team identity and job satisfaction. They also answered open questions about different aspects of their work. Answers were subjected to content analysis. Regression analyses were used to identify factors that predicted morale across groups. RESULTS: Psychiatrists had higher scores on emotional exhaustion and depersonalisation. There were no significant differences between the two groups in job satisfaction and job or role perception. Professionals reported positive relationships with patients as the most enjoyable aspects of their job, whilst team conflicts and high workloads were seen as most difficult to cope with. Multivariate analyses showed that being a psychiatrist and perceiving team conflicts as a main cause of pressure in the job predicted higher burnout. CONCLUSIONS: Simple open questions coupled with quantitative measures appear a promising tool to investigate morale of mental health professionals and identify factors determining morale. Research, training and service development should focus on relationship aspects both with patients and within teams to reduce burnout in CMHCs

User interface patterns in recommendation-empowered content intensive multimedia applications

Design Patterns (DPs) are acknowledged as powerful conceptual tools to improve design quality and to reduce time and cost of the development process by effect of the reuse of “good” design solutions. In many fields (e.g., software engineering, web engineering, interface design) patterns are widely used by practitioners and are also investigated from a research perspective. Still, they have been seldom explored in the arena of Recommender Systems (RSs). RSs provide suggestions (“recommendations”) for items that are likely to be appropriate for the user profile, and are increasingly adopted in content-intensive multimedia applications to complement traditional forms of search in large information spaces. This paper explores RSs through the lens of User Interface (UI) Design Patterns. We have performed a systematic analysis of 54 recommendation-empowered content-intensive multimedia applications, in order to: (i) discover the occurrences of existing domain independent UI patterns; (ii) identify frequently adopted UI solutions that are not modelled by existing patterns, and define a set of new UI patterns, some of which are specific of the interfaces for recommendation features while others can be useful also in a broader context. The results of our inspection have been discussed with and evaluated by a team of experts, leading to a consolidated set of 14 new patterns that are reported in the paper. Reusing pattern-based design solutions instead of building new solutions from scratch enables novice and expert designers to build good UIs for Recommendation-empowered content intensive multimedia applications more effectively, and ultimately can improve the UX experience in this class of systems. From a broader perspective, our work can stimulate future research bridging Recommender Systems, Web Engineering and Interface Design by means of Design Patterns, and highlights new research directions also discussed in the paper

User effort vs. accuracy in rating-based elicitation

One of the unresolved issues when designing a recommender system is the number of ratings -- i.e., the profile length -- that should be collected from a new user before providing recommendations. A design tension exists, induced by two conflicting requirements. On the one hand, the system must collect "enough"ratings from the user in order to learn her/his preferences and improve the accuracy of recommendations. On the other hand, gathering more ratings adds a burden on the user, which may negatively affect the user experience. Our research investigates the effects of profile length from both a subjective (user-centric) point of view and an objective (accuracy-based) perspective. We carried on an offline simulation with three algorithms, and a set of online experiments involving overall 960 users and four recommender algorithms, to measure which of the two contrasting forces influenced by the number of collected ratings -- recommendations relevance and burden of the rating process -- has stronger effects on the perceived quality of the user experience. Moreover, our study identifies the potentially optimal profile length for an explicit, rating based, and human controlled elicitation strategy

Multicriteria Decision Analysis and Conversational Agents for children with autism

Conversational agents has emerged as a new means of communication and social skills training for children with autism spectrum disorders (ASD), encouraging academia, industry, and therapeutic centres to investigate it further. This paper aims to develop a methodological framework based on Multicriteria Decision Analysis (MCDA) to identify the best , i.e. the most effective, conversational agent for this target group. To our knowledge, it is the first time the MCDA is applied to this specific domain. Our contribution is twofold: i) our method is an extension of traditional MCDA and we exemplify how to apply it to decision making process related to CA for person with autism: a methodological result that would be adopted for a broader range of technologies for person with impairments similar to ASD; ii) our results, based on the above mentioned method, suggest that Embodied Conversational Agent is most appropriate conversational technology to interact with children with ASD

Web extensions to UML: Using the MVC Triad

Abstract. Current Web Modelling Languages (WMLs) fall short of the requirements for the modelling of web system development. In particular, those WMLs with a hypermedia basis are more closely focussed on the information architecture whereas software system WMLs are more focussed on the functional architecture. Generally, modelling languages have failed to bridge the gap between these two areas. They also do not handle well the connection between different levels of abstraction and are largely unable to connect well with business models. Based on an analysis of existing modelling approaches, we propose a conceptual extension to modelling approaches that attempts to address these limitations. We show how it can implemented using UML modelling along with the addition of concepts taken from Web information modelling approaches, WebML in particular. The extensions are structured around the Model-View-Controller concept, which we argue provides an appropriate integrating modelling framework. We begin by discussing the scope and objectives of the extensions, followed by a description of the extensions themselves. We then illustrate the extensions by showing their application to a small case study.

Delivering Green Persuasion Strategies with a Conversational Agent: a Pilot Study

Climate change is undeniable. The drastic consequences it may have on our lives make a collective effort crucial. Our research explores how Conversational Agents (CAs) can persuade people into environmentally sustainable behaviors, particularly in domestic spaces where these technologies are becoming increasingly popular. In this research work, we conducted an empirical evaluation (N=29) exploring the effectiveness and stance towards the adoption of different persuasive strategies compared to a CA delivering messages referring to just one persuasion strategy. Furthermore, this contribution reports on a custom dialogue manager's implementation, designed to enable the execution of the experiment. Although study results suggested no significant difference in persuasion effectiveness and usability of the conversational agents, participants reported a significant difference in the perceptions of parasocial interactions and dialogue with the CA, preferring the one delivering multiple persuasive strategies

Emoty: an Emotionally Sensitive Conversational Agent for People with Neurodevelopmental Disorders

Our research aims at exploiting the advances in conversational technology to support people with Neurodevelopmental Disorder (NDD). NDD is a group of conditions that are characterized by severe deficits in the cognitive, emotional and motor areas and produce severe impairments in communication and social functioning. This paper presents the design, technology and exploratory evaluation of Emoty, a spoken Conversational Agent (CA) created specifically for individuals with NDD. The goal of Emoty is to help these persons enhancing communication abilities related to emotional recognition and expression, which are fundamental in any form of human relationship. The system exploits emotion detection capabilities based on the semantics of the speech by calling the IBM Watson Tone Analyzer API and from the harmonic features of the audio thanks to an “all-of-us” Deep Learning model. The design and evaluation of Emoty are based on the close collaboration among computer engineers and specialists in NDD (psychologists, neurological doctors, educators)