Factor structure and construct validity of the Adult Social Care Outcomes Toolkit for Carers (ASCOT-Carer)

Background: The ASCOT-Carer is a self-report instrument designed to measure social care-related quality of life (SCRQoL). This article presents the psychometric testing and validation of the ASCOT-Carer four response-level interview (INT4) in a sample of unpaid carers of adults who receive publicly-funded social care services in England. Methods: Unpaid carers were identified through a survey of users of publicly-funded social care services in England. 387 carers completed a face-to-face or telephone interview. Data on variables hypothesised to be related to SCRQoL (for example, characteristics of the carer, cared-for person and care situation) and measures of carer experience, strain, health-related quality of life and overall QoL were collected. Relationships between these variables and overall SCRQoL score were evaluated through correlation, ANOVA and regression analysis to test the construct validity of the scale. Internal reliability was assessed using Cronbach’s alpha and feasibility by the number of missing responses. Results: The construct validity was supported by statistically significant relationships between SCRQoL and scores on instruments of related constructs, as well as with characteristics of the carer and care recipient in univariate and multivariate analyses. A Cronbach’s alpha of 0.87 (7 items) indicates that the internal reliability of the instrument is satisfactory and a low number of missing responses (<1%) indicates a high level of acceptance. Conclusions: The results provide evidence to support the construct validity, factor structure, internal reliability and feasibility of the ASCOT-Carer INT4 as an instrument for measuring social care-related quality of life of unpaid carers who care for adults with a variety of long-term conditions, disability or problems related to old age

Dynamic behavior analysis via structured rank minimization

Human behavior and affect is inherently a dynamic phenomenon involving temporal evolution of patterns manifested through a multiplicity of non-verbal behavioral cues including facial expressions, body postures and gestures, and vocal outbursts. A natural assumption for human behavior modeling is that a continuous-time characterization of behavior is the output of a linear time-invariant system when behavioral cues act as the input (e.g., continuous rather than discrete annotations of dimensional affect). Here we study the learning of such dynamical system under real-world conditions, namely in the presence of noisy behavioral cues descriptors and possibly unreliable annotations by employing structured rank minimization. To this end, a novel structured rank minimization method and its scalable variant are proposed. The generalizability of the proposed framework is demonstrated by conducting experiments on 3 distinct dynamic behavior analysis tasks, namely (i) conflict intensity prediction, (ii) prediction of valence and arousal, and (iii) tracklet matching. The attained results outperform those achieved by other state-of-the-art methods for these tasks and, hence, evidence the robustness and effectiveness of the proposed approach

A listener model: introducing personality traits

International audienceWe present a computational model that generates listening behaviour for a virtual agent. It triggers backchannel signals according to the user's visual and acoustic behaviour. The appropriateness of the backchannel algorithm in a user-agent situation of storytelling, has been evaluated by naïve participants, who judged the algorithm-ruled timing of backchannels more positively than a random timing. The system can generate different types of backchannels. The choice of the type and the frequency of the backchannels to be displayed is performed considering the agent's personality traits. The personality of the agent is defined in terms of two dimensions, extroversion and neuroticism. We link agents with a higher level of extroversion to a higher tendency to perform more backchannels than introverted ones, and we link neuroticism to less mimicry production and more response and reactive signals sent. We run a perception study to test these relations in agent-user interactions, as evaluated by third parties. We find that the selection of the frequency of backchannels performed by our algorithm contributes to the correct interpretation of the agent's behaviour in terms of personality traits