Managing people and performance:An evidence based framework applied to health service organizations

People and their performance are key to an organization's effectiveness. This review describes an evidence-based framework of the links between some key organizational influences and staff performance, health and well-being. This preliminary framework integrates management and psychological approaches, with the aim of assisting future explanation, prediction and organizational change. Health care is taken as the focus of this review, as there are concerns internationally about health care effectiveness. The framework considers empirical evidence for links between the following organizational levels: 1. Context (organizational culture and inter-group relations; resources, including staffing; physical environment) 2. People management (HRM practices and strategies; job design, workload and teamwork; employee involvement and control over work; leadership and support) 3. Psychological consequences for employees (health and stress; satisfaction and commitment; knowledge, skills and motivation) 4. Employee behaviour (absenteeism and turnover; task and contextual performance; errors and near misses) 5. Organizational performance; patient care. This review contributes to an evidence base for policies and practices of people management and performance management. Its usefulness will depend on future empirical research, using appropriate research designs, sufficient study power and measures that are reliable and valid.

Evidence-based nanoscopic and molecular framework for excipient functionality in compressed orally disintegrating tablets

The work investigates the adhesive/cohesive molecular and physical interactions together with nanoscopic features of commonly used orally disintegrating tablet (ODT) excipients microcrystalline cellulose (MCC) and D-mannitol. This helps to elucidate the underlying physico-chemical and mechanical mechanisms responsible for powder densification and optimum product functionality. Atomic force microscopy (AFM) contact mode analysis was performed to measure nano-adhesion forces and surface energies between excipient-drug particles (6-10 different particles per each pair). Moreover, surface topography images (100 nm2-10 μm2) and roughness data were acquired from AFM tapping mode. AFM data were related to ODT macro/microscopic properties obtained from SEM, FTIR, XRD, thermal analysis using DSC and TGA, disintegration testing, Heckel and tabletability profiles. The study results showed a good association between the adhesive molecular and physical forces of paired particles and the resultant densification mechanisms responsible for mechanical strength of tablets. MCC micro roughness was 3 times that of D-mannitol which explains the high hardness of MCC ODTs due to mechanical interlocking. Hydrogen bonding between MCC particles could not be established from both AFM and FTIR solid state investigation. On the contrary, D-mannitol produced fragile ODTs due to fragmentation of surface crystallites during compression attained from its weak crystal structure. Furthermore, AFM analysis has shown the presence of extensive micro fibril structures inhabiting nano pores which further supports the use of MCC as a disintegrant. Overall, excipients (and model drugs) showed mechanistic behaviour on the nano/micro scale that could be related to the functionality of materials on the macro scale. © 2014 Al-khattawi et al.

Testing the assertion that 'local food is best':the challenges of an evidence-based approach

Advocates of ‘local food’ claim it serves to reduce food miles and greenhouse gas emissions, improve food safety and quality, strengthen local economies and enhance social capital. We critically review the philosophical and scientific rationale for this assertion, and consider whether conventional scientific approaches can help resolve the debate. We conclude that food miles are a poor indicator of the environmental and ethical impacts of food production. Only through combining spatially explicit life cycle assessment with analysis of social issues can the benefits of local food be assessed. This type of analysis is currently lacking for nearly all food chains.

Proof in the pedagogic pudding? The challenges of promoting evidence-based teaching practice in a research-focused institution

Drawing upon the findings of a mixed methodological study, this paper critically analyses the cultural, pedagogical, and organisational issues encountered by academics and support staff working within a newly established Centre for Learning Innovation and Professional Practice tasked with facilitating a new teaching-focused policy in a previously research-led institution. The aim of this policy is to assure that, across the institution, teaching is given the same priority and kudos as research. Focusing specifically staff perceptions of the impact of the new policy on various aspects of academic life including, pedagogic practice, student support, staff training, and organisational management, the paper critically addresses the cultural and attitudinal challenges of change management (Kotter, 1996) within a ‘grey-brick’ university. In doing so it makes a significant contribution to current academic theory and debate in the areas of pedagogic practice and organisational management.

The development and evaluation of an evidence-based drug information service

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Evidence based pharmacogenetics study of the use of anti-psychotics in treatment of schizophrenia

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Evidence based assessment of the cardiovascular adverse effects of specific cyclooxy-genase-2 inhibitors

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Expanding the evidence within evidence-based healthcare:thinking about the context, acceptability and feasibility of interventions

Evidence-based medicine is crucial to contemporary healthcare. It is dependent on systematic review methodology modelled on an arguably inadequate hierarchy of evidence. There has been a significant increase in medical and health research using qualitative and mixed method designs. The perspective taken in this article is that we need to broaden our evidence base if we are to fully take account of issues of context, acceptability and feasibility in the development and implementation of healthcare interventions. One way of doing this is to use a range of methods that better fit the different aspects of intervention development and implementation. Methods for the systematic review of evidence, other than randomised-controlled trials, are available and there is a readiness to incorporate these other types of evidence into good-practice guidance, but we need a clear methodology to translate these advances in research into the world of policy.

Privacy and the City:user identification and location semantics in location-based social networks

With the advent of GPS enabled smartphones, an increasing number of users is actively sharing their location through a variety of applications and services. Along with the continuing growth of Location-Based Social Networks (LBSNs), security experts have increasingly warned the public of the dangers of exposing sensitive information such as personal location data. Most importantly, in addition to the geographical coordinates of the user’s location, LBSNs allow easy access to an additional set of characteristics of that location, such as the venue type or popularity. In this paper, we investigate the role of location semantics in the identification of LBSN users. We simulate a scenario in which the attacker’s goal is to reveal the identity of a set of LBSN users by observing their check-in activity. We then propose to answer the following question: what are the types of venues that a malicious user has to monitor to maximize the probability of success? Conversely, when should a user decide whether to make his/her check-in to a location public or not? We perform our study on more than 1 million check-ins distributed over 17 urban regions of the United States. Our analysis shows that different types of venues display different discriminative power in terms of user identity, with most of the venues in the “Residence” category providing the highest re-identification success across the urban regions. Interestingly, we also find that users with a high entropy of their check-ins distribution are not necessarily the hardest to identify, suggesting that it is the collective behaviour of the users’ population that determines the complexity of the identification task, rather than the individual behaviour.

It's the way you check-in:identifying users in location-based social networks

In recent years, the rapid spread of smartphones has led to the increasing popularity of Location-Based Social Networks (LBSNs). Although a number of research studies and articles in the press have shown the dangers of exposing personal location data, the inherent nature of LBSNs encourages users to publish information about their current location (i.e., their check-ins). The same is true for the majority of the most popular social networking websites, which offer the possibility of associating the current location of users to their posts and photos. Moreover, some LBSNs, such as Foursquare, let users tag their friends in their check-ins, thus potentially releasing location information of individuals that have no control over the published data. This raises additional privacy concerns for the management of location information in LBSNs. In this paper we propose and evaluate a series of techniques for the identification of users from their check-in data. More specifically, we first present two strategies according to which users are characterized by the spatio-temporal trajectory emerging from their check-ins over time and the frequency of visit to specific locations, respectively. In addition to these approaches, we also propose a hybrid strategy that is able to exploit both types of information. It is worth noting that these techniques can be applied to a more general class of problems where locations and social links of individuals are available in a given dataset. We evaluate our techniques by means of three real-world LBSNs datasets, demonstrating that a very limited amount of data points is sufficient to identify a user with a high degree of accuracy. For instance, we show that in some datasets we are able to classify more than 80% of the users correctly.