Outcomes and Well-being Part 2: A comparative longitudinal study of two models of homecare delivery and their impact upon the older person self-reported subjective well-being. A qualitative follow up study paper

Purpose – This paper aims to follow up on a previous quantitative research project which established that outcome-focussed care appeared to be associated with an increase in the individuals’ subjective well-being. The purpose of this paper is to establish why the intervention enabled this. Design/methodology/approach – The study utilised a qualitative approach to gather the subjective experience of the individual service users. The sample consisted of 20 service users, who were subject of two semi-structured interviews; one interview at the start of the intervention and one at the six month stage. The data were then analysed under core themes raised by the service user in these interviews. The sample was divided into two, with one group receiving the outcome-focussed model of care and the other group receiving the traditional time focussed care. Findings – The research established that service users’ subjective well-being improved due to the ability of outcome-focussed care to provide consistency, flexibility and most importantly the ability of the service user to form a relationship with the homecare workers providing their care. Practical implications – This paper will assist professionals to understand why outcome-focus care has a profound impact upon service users’ subjective well-being as opposed to the existing task focussed care. Originality/value – This and the previous paper provide an insight into how different processes and models of intervention impact upon the subjective well-being of socially isolated older people

Quantitative Verification: Formal Guarantees for Timeliness, Reliability and Performance

Computerised systems appear in almost all aspects of our daily lives, often in safety-critical scenarios such as embedded control systems in cars and aircraft or medical devices such as pacemakers and sensors. We are thus increasingly reliant on these systems working correctly, despite often operating in unpredictable or unreliable environments. Designers of such devices need ways to guarantee that they will operate in a reliable and efficient manner. Quantitative verification is a technique for analysing quantitative aspects of a system's design, such as timeliness, reliability or performance. It applies formal methods, based on a rigorous analysis of a mathematical model of the system, to automatically prove certain precisely specified properties, e.g. ``the airbag will always deploy within 20 milliseconds after a crash'' or ``the probability of both sensors failing simultaneously is less than 0.001''. The ability to formally guarantee quantitative properties of this kind is beneficial across a wide range of application domains. For example, in safety-critical systems, it may be essential to establish credible bounds on the probability with which certain failures or combinations of failures can occur. In embedded control systems, it is often important to comply with strict constraints on timing or resources. More generally, being able to derive guarantees on precisely specified levels of performance or efficiency is a valuable tool in the design of, for example, wireless networking protocols, robotic systems or power management algorithms, to name but a few. This report gives a short introduction to quantitative verification, focusing in particular on a widely used technique called model checking, and its generalisation to the analysis of quantitative aspects of a system such as timing, probabilistic behaviour or resource usage. The intended audience is industrial designers and developers of systems such as those highlighted above who could benefit from the application of quantitative verification,but lack expertise in formal verification or modelling

Verification and control of partially observable probabilistic systems

We present automated techniques for the verification and control of partially observable, probabilistic systems for both discrete and dense models of time. For the discrete-time case, we formally model these systems using partially observable Markov decision processes; for dense time, we propose an extension of probabilistic timed automata in which local states are partially visible to an observer or controller. We give probabilistic temporal logics that can express a range of quantitative properties of these models, relating to the probability of an event’s occurrence or the expected value of a reward measure. We then propose techniques to either verify that such a property holds or synthesise a controller for the model which makes it true. Our approach is based on a grid-based abstraction of the uncountable belief space induced by partial observability and, for dense-time models, an integer discretisation of real-time behaviour. The former is necessarily approximate since the underlying problem is undecidable, however we show how both lower and upper bounds on numerical results can be generated. We illustrate the effectiveness of the approach by implementing it in the PRISM model checker and applying it to several case studies from the domains of task and network scheduling, computer security and planning

Technical demands of soccer match-play in the English Championship

The aim of this study was to investigate the effect of match-play on the performance of technical actions in professional soccer players. Using computerized notational analysis, technical performance was quantified for the outfield players of one team during the 2010/2011 English Championship season. This retrospective study evaluated temporal patterns in the performance of players who completed more than 10 games (n=10). Total possessions and number of ball distributions were lower in the second versus the first half of match-play (10 ± 7%, P=0.010 and 11 ± 8% P=0.009,respectively). Analysis across 15-min intervals revealed reductions during the last 15-min of match-play in the total number of possessions (0:00-14:59 min: 11.8 ± 1.9 vs.75:00-89:59 min: 9.5 ± 1.7, P<0.05) and distributions (0:00-14:59 min: 10.9 ± 2.3 vs.75:00-89:59 min: 8.7 ± 2.1, P<0.05). The number of touches taken per possession, number of challenges, percentage of challenges won, length of forward distributions and percentage success of distributions were all similar between halves and across 15-min intervals. These results demonstrate that match-specific factors reduced total possessions and number of passes in the second half of match-play. Coaching staff could use this information to inform team tactics and technical training sessions