Benefits and losses: a qualitative study exploring healthcare staff perceptions of teamworking

ABSTRACT Objectives: To examine staff perceptions of teamworking practice in the field of stroke care. Design: Qualitative interview study. Setting: Three teams providing care to patients with stroke across a typical care pathway of acute hospital ward, specialist stroke unit, and community rehabilitation. Participants: 37 staff members from a range of professions. Main outcome measures: Healthcare staff perceptions of teamworking. Results: Through detailed coding and analysis of the transcripts, five perceptions regarding the impact of teamworking on staff and patients were identified. These were: (1) mutual staff support, (2) knowledge and skills sharing, (3) timely intervention/discharge, (4) reduced individual decision-making and responsibility and (5) impact on patient contact time. Conclusions: Teamworking practice may be associated with a number of perceived benefits for staff and patient care; however, the potential for losses resulting from reduced patient contact time and ill-defined responsibility needs further investigation

Protecting Future Personal Computing: Challenging Traditional Network Security Models

The Internet is a notoriously two-way street. If multiple computers can communicate sensitive data across the internet, malicious entities can access the network and collect this data also. The range and number of connected devices is increasing dramatically and with this expansion so is the security risk. Collection of ever rising quantities of data, especially sensitive and personal data, raises many challenges and questions about the suitability of current security. The key problem our research investigates is how we can adapt traditional security models to enhance it both current and future deployment. The work is not aimed to replace existing security although it builds upon it to complement it and enhance existing methods. We utilise the timeliness of the Internet of Things as a focus to develop and experiment with our work. In this paper we present our novel framework and introduce our initial work to prove the concept is feasible. Our initial results are encouraging as to the impact the framework could have on future security. Keywords- Network security; mobile security; smartphone; malware detection; in-network; Collaborative; Internet of Thing

Lightweight Forensics Application: Lightweight Approach to Securing Mobile Devices

Physical objects with the addition of sensors, actuators and a connection to the internet form devices which can collect, process and communicate data to each other. Devices may not have been designed with connectivity in mind and adding it as an afterthought is problematic. This provides a significant technical challenge concerning securing the devices, as they are all of a sudden open to a wide range of attacks whilst providing more opportunities for malicious users and increases the chances of device compromise. The key aim of this research is to address limitations in current security solutions on mobile devices by defining a novel approach which will sustain future advances in mobile technology. Using combined security techniques our proposed solution will work with existing security technology to create a more effective and successful security implementation that will be suitable for a wide range of mobile devices. Keywords- Lightweight security; mobile device; smartphone; digital forensics; malware detection; in-network; Collaborative; Internet of Thing

Automatic recognition of children’s read speech for stuttering application

Stuttering is a common speech disfluency that may persist into adulthood if not treated in its early stages. Techniques from spoken language understanding may be applied to provide auto-mated diagnoses of stuttering from voice recordings; however,there are several difficulties, including the lack of training data involving young children and the high dimensionality of these data. This study investigates how automatic speech recognition(ASR) could help clinicians by providing a tool that automatically recognises stuttering events and provides a useful written transcription of what was said. In addition, to enhance the performance of ASR and to alleviate the lack of stuttering data, this study examines the effect of augmenting the language model with artificially generated data. The performance of the ASR tool with and without language model augmentation is com-pared. Following language model augmentation, the ASR tool’s performance improved recall from 38% to 62.2% and precision from 56.58% to 71%. When mis-recognised events are more coarsely classified as stuttering/ non-stuttering events, the performance improves up to 73% in recall and 84% in precision.Although the obtained results are not perfect, they map to fairly robust stutter/ non-stutter decision boundaries

Detecting stuttering events in transcripts of children’s speech

Stuttering is a common problem in childhood that may persist into adulthood if not treated in early stages. Techniques from spoken language understanding may be applied to provide automated diagnosis of stuttering from children speech. The main challenges however lie in the lack of training data and the high dimensionality of this data. This study investigates the applicability of machine learning approaches for detecting stuttering events in transcripts. Two machine learning approaches were applied, namely HELM and CRF. The performance of these two approaches are compared, and the effect of data augmentation is examined in both approaches. Experimental results show that CRF outperforms HELM by 2.2% in the baseline experiments. Data augmentation helps improve systems performance, especially for rarely available events. In addition to the annotated augmented data, this study also adds annotated human transcriptions from real stuttered children’s speech to help expand the research in this field

Evaluating emotional well-being in the person with acquired communication disability: a multidisciplinary approach

The methods for evaluating emotional well-being in the stroke population and specifically, the aphasic population, have been shown to be varied across NHS services in the UK. In spite of this, mood disorders are very common after stroke. Although there is some variation in reports, up to 50% of stroke patients will suffer with depression at some point following their stroke (Gordon and Hibbard, 1997) and this will include aphasic speakers. The guidance from the National Institute for Clinical Excellence (NICE, 2004) states that ‘patients should be screened for depression and anxiety within the first month of stroke and their mood kept under review’ (Guideline 4.1.1.c). However, there are well recognised difficulties in evaluating well-being in the aphasic speaker (Code and Herrman 2003) because of problems in making test items comprehensible