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Burnout has been identified as a significant problem for healthcare professionals in the UK, and is associated with reduced morale, increased absenteeism and high rates of staff turnover. Although burnout was recently added to the International Classification of Diseases, the term is not used consistently within the existing literature nor in the clinical environment. This research used a critical discursive psychology approach to consider how burnout was spoken about and subsequently how the meaning of the term was socially constructed. A team focus group design illustrated seven main descriptions (interpretative repertoires) of burnout. These repertoires were often contradictory and created ideological dilemmas as participants negotiated the use of conflicting repertoires. Participants most commonly positioned burnout as unacceptable and used a variety of rhetorical devices to distance themselves from burnout. The findings suggest that the meaning of burnout is more nuanced than the literature and the ICD-11 definition implies.

This article was submitted as part of a research portfolio for partial fulfilment of the Doctorate in Clinical Psychology. Reflective practice is an essential and assessed component of clinical psychology doctoral training programmes. Despite the numerous methods training programmes utilise to develop reflection, previous literature has mainly focussed on individual methods (e.g. reflective practice groups). In addition, the use of reflection as an assessed competency has received criticism within the wider healthcare literature. The purpose of this study was to explore trainee clinical psychologists’ broader ‘reflective journey’ and any dilemmas they encountered. Ten trainee clinical psychologists were interviewed and asked to describe their ‘reflective journeys’ as they approached qualification. Transcripts were analysed using three narrative analysis techniques: thematic, structural, and performative/dialogic . Trainees typically organised the narrative of their reflective journey to illustrate growth and development throughout the training period, storying reflection as an important part of their role. However, trainees also narrated the enactment of reflection as problematic with three overarching dilemmas: What does ‘the structure’ allow?; Am I able to be open?; and What are the consequences of being open? Based on these findings, a critical consideration for clinical psychology training is that the current reflective context may be counter-intuitive in enabling trainees to reflect openly and authentically.

Machine learning is fast becoming a cornerstone in many data analytic, image processing and scientific computing applications. Depending on the deployment scale, these tasks can either be performed on embedded devices, or larger cloud computing platforms. However, one key trend is an exponential increase in the required compute power as data is collected and processed at a previously unprecedented scale. In an effort to reduce the computational complexity there has been significant work on reduced precision representations. Unlike Central Processing Units, Graphical Processing Units and Applications Specific Integrated Circuits which have fixed datapaths, Field Programmable Gate Arrays (FPGA) are flexible and uniquely positioned to take advantage of reduced precision representations. This thesis presents FPGA architectures for low precision machine learning algorithms, considering three distinct levels: the application, the framework and the operator. Firstly, a spectral anomaly detection application is presented, designed for low latency and real-time processing of radio signals. Two types of detector are explored, a neural network autoencoder and least squares bitmap detector. Secondly, a generalised matrix multiplication framework for the Intel HARPv2 is outlined. The framework was designed specifically for machine learning applications; containing runtime configurable optimisations for reduced precision deep learning. Finally, a new machine learning specific operator is presented. A bit-dependent multiplication algorithm designed to conditionally add only the relevant parts of the operands and arbitrarily skip over redundant computation. Demonstrating optimisations on all three levels; the application, the framework and the operator, illustrates that FPGAs can achieve state-of-the-art performance in important machine learning workloads where high performance is critical; while simultaneously reducing implementation complexity.