Bradford Scholars is the University of Bradford online research archive. Access is free to anyone interested in research being conducted at Bradford. In the repository you will find a range of materials from journal articles and conference papers to research reports and theses.
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The assessment and management of medicine-related risks associated with hospital readmission for older people living with frailtyOlder people living with frailty are at a higher risk of medication-related incidents due to frequent hospitalisation, complex health needs and polypharmacy. There is evidence that identifying patients at high risk of hospital readmission can enhance the impact of interventions to prevent readmission. However, there is insufficient evidence of the role of medication in readmission in this vulnerable patient group, and what pharmacists can do to reduce readmissions. This research used a mixed-method approach to examine the association between medicines-related risks and readmissions, and the pharmacists’ interventions thought to be important by key stakeholders to reduce readmissions. Medicines-related risks such as polypharmacy, potentially inappropriate medicines and high risk medicines did not have a strong association with repeated hospital admission in multivariable logistic regression. Patients who had multi-morbidities, and non-supported discharge, had a higher risk of repeated hospital admissions. A consensus survey study with three iterative rounds identified a list of pharmacists’ interventions viewed as high priority for reducing readmissions in frail elderly patients. The interventions with the highest scores included medicines reconciliation at discharge, on admission, preparation of discharge summary, provision of tailored patient education about medicines and inter-disciplinary working in ward rounds. A systematic intervention development method was used to further develop an intervention, underpinned by the theoretical domains framework. There is a need to further explore the role of medication-related risks in contributing to readmission using other validated tools and larger datasets. This could be used to inform development of future risk stratification tools to identify high risk patients in order to target interventions to maximise its impact.
Ethical Human Resource Management and Employee Welfare: Empirical Perspectives from the Bangladeshi RMG SectorThis study explores employee welfare and working conditions in relation to ethical HRM practices from the employees’ perspective in the Bangladeshi Ready-Made Garment (RMG) sector. This research is inspired by the need to understand the challenges that employees face in their practical work settings and the unfair Human Resource Management (HRM) process that they experience in their work. The interpretivist philosophical approach and the qualitative research approach have been adopted in this research study, while the semi-structured interview method has been applied for primary-data collection. A total of 25 semi-structured interviews with General Employees, Informal Representative Leaders, Employees, Middle and Senior Managers have been undertaken in this process. Five focus-group discussions have also been applied to corroborate the data generated from the 25 semi-structured interviews. The case-study strategy has also been implemented as a research strategy and thematic analysis has been applied to the data-analysis process. The findings of this research study show the need for deeper understanding and application of ethical HRM practices in particular national and sectoral contexts, specifically in the Bangladeshi RMG sector. These ethical HRM practices include, but are not limited to, the initiation of rights-based understanding and respect-based perception, the inclusion of welfare facilities, the implementation of a fair payment policy, the equitable recruitment and selection policy, and the initiation and equality of training and development facilities. These new ethical understandings contribute to the field of ethical HRM in the context of the development of employee welfare and decent working conditions in this sector.
The Influence of Islamic Work Ethic on Employees’ Responses Towards Organizational Change: An Empirical Investigation on Islamic Banks in KuwaitThe corporate world today is highly competitive and in order for organizations to survive and remain competitive, they must constantly evolve through change. However, the majority of organizational changes neither result in successful implementation or foster sustained change. It is suggested that the success of changes are highly contingent on employees’ responses towards them. To this end, Islamic Work Ethic (IWE) has become a subject of growing interest amongst academia and human resource literature attempting to understand and predict employees’ responses towards organizational change, particularly in Muslim societies. Despite this, studies attempting to uncover IWE’s influence on characteristics of employees’ responses towards change have revealed varying outcomes. Thus, the nature of the relationship remains ambiguous. To tackle this gap, this study contributes to knowledge by developing a conceptual model that assists in identifying the influence of IWE on employees’ responses towards change in the shape of their commitment to change and organizational deviance behaviors. The testing of these relationships was carried out in the ever changing and developing Islamic banking industry within the Middle Eastern context of Kuwait. Through a quantitative case-study approach, data was collected from 398 branch-level employees via questionnaires. The outcomes revealed that the extent of IWE’s influence on employee commitment to change varied greatly across different components (affective, normative and continuance). On the other hand, IWE was found to negatively influence employee engagement in organizational deviance towards change. Due to such findings, several theoretical implications, practical recommendations and future research directions are put forward.
The use of silent substitution in measuring isolated cone- & rod- human electroretinograms. An electrophysiological study of human rod- and cone- photoreceptor activity derived using silent substitution paradigmAfter over a decade of its discovery, the Electroretinogram (ERG) still remains the objective tool that is conventionally used in assessment of retinal function in health and disease. Although there is ongoing research in developing ERG- recording techniques, interpretation and clinical applications, there is still a limited understanding on how each photoreceptor class contribute to the ERG waveform and their role and/or susceptibilities in various retinal diseases still remains unclear. Another limitation with currently used conventional testing protocols in a clinical setting is the requirement of an adaptation period which is time-consuming. Furthermore, the ERG responses derived in this manner are recorded under different stimulus conditions, thus, making comparison of these signals difficult. To address these issues and develop a new testing method, we employed silent substitution paradigm in obtaining cone- and rod- isolating ERGs using sine- and square- wave temporal profiles. The ERGs achieved in this manner were shown to be photoreceptor-selective. Furthermore, these responses did not only provide the functional index of photoreceptors but their contributions to their successive postreceptoral pathways. We believe that the substitution stimuli used in this thesis could be a valuable tool in functional assessment of individual photoreceptor classes in normal and pathological conditions. Furthermore, we speculate that this method of cone/rod activity isolation could possibly be used in developing faster and efficient photoreceptor-selective testing protocols without the need of adaptation.
Contributions for Handling Big Data Heterogeneity. Using Intuitionistic Fuzzy Set Theory and Similarity Measures for Classifying Heterogeneous DataA huge amount of data is generated daily by digital technologies such as social media, web logs, traffic sensors, on-line transactions, tracking data, videos, and so on. This has led to the archiving and storage of larger and larger datasets, many of which are multi-modal, or contain different types of data which contribute to the problem that is now known as “Big Data”. In the area of Big Data, volume, variety and velocity problems remain difficult to solve. The work presented in this thesis focuses on the variety aspect of Big Data. For example, data can come in various and mixed formats for the same feature(attribute) or different features and can be identified mainly by one of the following data types: real-valued, crisp and linguistic values. The increasing variety and ambiguity of such data are particularly challenging to process and to build accurate machine learning models. Therefore, data heterogeneity requires new methods of analysis and modelling techniques to enable useful information extraction and the modelling of achievable tasks. In this thesis, new approaches are proposed for handling heterogeneous Big Data. these include two techniques for filtering heterogeneous data objects are proposed. The two techniques called Two-Dimensional Similarity Space(2DSS) for data described by numeric and categorical features, and Three-Dimensional Similarity Space(3DSS) for real-valued, crisp and linguistic data are proposed for filtering such data. Both filtering techniques are used in this research to reduce the noise from the initial dataset and make the dataset more homogeneous. Furthermore, a new similarity measure based on intuitionistic fuzzy set theory is proposed. The proposed measure is used to handle the heterogeneity and ambiguity within crisp and linguistic data. In addition, new combine similarity models are proposed which allow for a comparison between the heterogeneous data objects represented by a combination of crisp and linguistic values. Diverse examples are used to illustrate and discuss the efficiency of the proposed similarity models. The thesis also presents modification of the k-Nearest Neighbour classifier, called k-Nearest Neighbour Weighted Average (k-NNWA), to classify the heterogeneous dataset described by real-valued, crisp and linguistic data. Finally, the thesis also introduces a novel classification model, called FCCM (Filter Combined Classification Model), for heterogeneous data classification. The proposed model combines the advantages of the 3DSS and k-NNWA classifier and outperforms the latter algorithm. All the proposed models and techniques have been applied to weather datasets and evaluated using accuracy, Fscore and ROC area measures. The experiments revealed that the proposed filtering techniques are an efficient approach for removing noise from heterogeneous data and improving the performance of classification models. Moreover, the experiments showed that the proposed similarity measure for intuitionistic fuzzy data is capable of handling the fuzziness of heterogeneous data and the intuitionistic fuzzy set theory offers some promise in solving some Big Data problems by handling the uncertainties, and the heterogeneity of the data.