Saudi Cultural Missions Theses & Dissertations
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Item Restricted Hospital-Based Nurses' Experiences of Caring for Dementia Patients: A Systematic Review(Queen's University Belfast, 2024-10) Musawi, Abdullah; Brown, Michael; Galway, KarenBackground As the prevalence of dementia rises globally, with notable increases being seen in regions such as Saudi Arabia due to an ageing population, it is essential to understand patients’ experiences to effectively address negative incidents that hinder quality care and improve nursing education, healthcare organisation environments, and nursing practice. Objectives The objectives of this study are to (1) synthesise qualitative evidence regarding hospital staff experiences in caring for PLWD, (2) identify barriers and facilitators in providing person-centred dementia care, and (3) explore the impacts of this care on nurses, including challenges such as workload and stress, as well as positive aspects like fulfilment and satisfaction. Methodology A comprehensive search was conducted across credible databases, such as PubMed and EBSCO, focusing on studies published from 2019 to 2022, and guided by inclusion and exclusion criteria to ensure relevance and quality. Study quality was assessed using the CASP checklist, and findings were synthesised using meta-aggregation, with similar findings being grouped into categories to allow for a structured narrative synthesis. Findings Identified themes from the ten selected studies were as follows: 1) emotional impact and satisfaction, 2) challenges in providing person-centred care, 3) the importance of specialised knowledge and skills, 4) collaboration and teamwork, and 5) adaptability in medication management. Experiences encompassed a range of emotions, including frustration and fulfilment, communication issues, lack of training, and environmental barriers. Limitations included a small sample size of only ten studies and the exclusion of non-English and quantitative research. This potentially skewed the representativeness and depth of the findings while overlooking variations in experiences influenced by nurses' backgrounds or specific training. Conclusion These findings underscore the need for targeted interventions that support nurses' professional development and emotional health in dementia care settings.25 0Item Restricted The Effectiveness of Fcial Cues for Automatic Detection of Cognitive Impairment Using In-the-wild Data(Saudi Digital Library, 2023-11-30) Alzahrani, Fatimah; Christensen, Heidi; Maddock, SteveThe development of automatic methods for the early detection of cognitive impairment (CI) has attracted much research interest due to its crucial role in helping people get suitable treatment or care. People with CI may experience various changes in their facial cues, such as eye blink rate and head movement. This thesis aims to investigate the use of facial cues to develop an automatic system for detecting CI using in-the-wild data. Firstly, the 'in-the-wild data' term is defined, and associated challenges are identified by analysing datasets used in previous work. In-the-wild data can affect the reliability of the performance of state-of-the-art approaches. Second, this thesis investigates the automatic detection of neurodegenerative disorder, mild cognitive impairment and functional memory disorder, showing the applicability of detecting health conditions with similar symptoms. Then, a novel multiple thresholds (MTs) approach for detecting an eye blink rate feature is introduced. This approach addresses in-the-wild data challenges by generating multiple thresholds, resulting in a vector of blink rates for each participant. Then, the feasibility of this feature in detecting CI is examined. Other features considered are head turn rate, head turn statistical features, head movement statistical features and low-level features. The results show that these facial features significantly distinguish different health conditions.15 0Item Restricted The Relationship Between Stigma and Level of Alzheimer's Disease Knowledge Within The Saudi Culture(Saudi Digital Library, 2023-11-30) Jambi, Amnah; Butcher, Howard KarlThere are two types of stigmas: self-stigma and public stigma. The focus of this dissertation was public stigma. The public stigma encountered by persons with Alzheimer’s Disease (AD) contributes to the isolation of families due to the effort made by AD caregivers to adjust to social challenges (Abojabel & Warner, 2019). According to the Saudi Alzheimer’s Disease Association (2022), there are 130 thousand documented cases of AD, which comprised 9% of the aged population. The severity of stigmas can vary across cultures because stigmas of disease are connected to cultural norms (Corrigan, 2014). Most studies conducted in Saudi Arabia have assessed public stigma regarding mental illnesses, but no study has been found regarding public stigma within the AD scope. Population-based approaches that attempt to clarify stigma level prevalence in representative samples are important to develop methods to address these disparities and ensure equitable access to health care within the population's cultural context. The aim of this study was to 1) identify the relationship between public stigma and the level of AD knowledge among the Saudi population and 2) identify the potential factors that were associated with public stigma and AD knowledge levels among Saudi community members, within the context of a caring science perspective using critical caring theory and specific-situation theory. A non-experimental, correlational descriptive, and cross-sectional design was used for this study. The method of collecting data was an online survey method (Qualtrics) using the Basic Knowledge of Alzheimer's Disease (BKAD) to measure knowledge (Wiese, et al., 2017, 2019), and an adapted version of the Attribution Questionnaire AQ-9 to measure public stigma (Kim et al., 2021; Werner et al., 2017). Data analysis was performed via SPSS version 29. A total of (N = 150) participants were recruited in a span of three months. Data analysis revealed: 1) a significant correlation (r = -.20, p = .016) between AD knowledge and public stigma level, 2) significant factors associated with public stigma level were gender (B = 1.89, t = 2.51, p = .013), an education level (B = -2.69, t = -3.42, p < .001); and experience as an AD caregiver professionally (B = 2.69, t = 2.30, p = .023), 3) Factors significantly associated with AD knowledge level were the a) age group 18- 24 years old (B = 2.78, t = 2.27, p = .025), b) occupation in the non-medical profession category (B = -1.77, t = -2.04, p = .043), and c) education level (B = 2.27, t = 2.75, p = .007). Stigma can vary based on various contextual factors, including cultural influences, in which further studies are needed to better understand the concept in versatile cultures. The findings provided valuable insights into the patterns and significance of relationships between public stigma, AD knowledge, and factors associated with stigma and AD knowledge.40 0Item Restricted Investigating Rule Induction Methods in Machine Learning for Improving Medical Dementia Prediction(2023-08-04) Albalawi, Hadeel; Lambrou, TryphonAlzheimer’s Disease (AD) is a neurodegenerative disease related to dementia that predominantly affects the elderly population with symptoms including, but not limited to, cognitive impairment and memory loss. Detecting AD and other conditions like Mild Cognitive Impairment (MCI) can lengthen the lifespan of patients and help them to access the medical services. One approach to achieve a rapid and early diagnosis of AD is using data mining (DM) techniques, which can search various characteristic traits related to Cognitively Normal (CN), AD, and MCI data observations to build classifiers that reveal contributors to the disease. Classifiers developed by DM techniques are used by medical professionals during dementia clinical processes to help make correct diagnosis. In this research, we amend a process based on DM that evaluates characteristics related to dementia conditions, in particular AD. The novelty of the proposed process lies in the classification algorithm that we have named ‘Rules-based Uncertainty Reduction Algorithm’ (RURA). RURA develops classifiers with rules, which strengthen the decisions that can be invoked by the medical professional when evaluating patients with dementia. Empirical evaluation, using several DM algorithms were conducted on biological marker (biomarkers) and behavioral characteristics of data subjects collected from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) data project to analyze the effectiveness of the proposed classification algorithm. The results show that RURA perform over 90% in predicting AD when compared with classifiers developed by other DM algorithms. Furthermore, the results show that delayed word recall and orientation are effective cognitive factors that contribute to the ability to detect dementia early. For the biomarkers, ABETA and neurofibrillary tangles in the neurons showed some associations with AD although fewer than those of the cognitive elements. Moreover, the results obtained show that the classifiers developed by the RURA algorithm from psychological attributes (Subset 2) are higher in accuracy than the other DM algorithms. The results from Subset 2, show that RURA’s classifiers are higher with 11.28%, 1.18%, 1.88%, 3.75%, 1.70%, and 0.27% than PRISM, Nnge, kNN, Naïve Bayes, ORule, and Ridor, separately. Also, RURA developed higher accurate classifiers than the remaining DM algorithms on Subset 12 which includes cognitive attributes and biomarker attributes. However, the performance of RURA did not improve when the general attributes were added to the psychological attributes and the accuracy decreased by 1.34% when mining Subset 2 (psychological attributes and general attributes). More considerably, the results indicate that specific biomarkers in the ADNI-MERGE dataset when used alone by a DM algorithm that we considered for dementia detection often will not result in acceptable predictive systems. For example, when DM algorithms were trained on MRI and PET attributes (Subset 5), the classifiers created had classification accuracies of 42–55% which are relatively low. So, utilizing neuroimaging attributes in the ADNI-MERGE dataset to detect dementia is not ideal using the DM techniques, especially when a baseline visit is used to represent each data subject. Therefore, the biomarkers attributes must be accompanied with psychological elements to improve the detection rate of dementia.12 0