The purpose of the study was to explore and describe the lived experiences of general nurses working at Standerton hospital medical wards which also admit psychiatric patients. A qualitative, descriptive phenomenological approach was used for the study. The study population consisted of seven general nurses working in medical wards at Standerton hospital. Purposive sampling was used to select participants who met the inclusion criteria. Researcher used in-depth face to face interviews to collect data until data saturation was achieved. Tesch’s method of qualitative data analysis was utilised to identify themes. Three themes and five sub-themes emerged from the study: theme1: perceived danger due to aggression sub-themes stress for medical patients, stress for medical patients’ families and stress for nurses. Theme 2: lack of skills in dealing with psychiatric patients’ sub- theme use of restrains. Theme 3: self fulfilling prophecy subtheme reported incidences. The study findings demonstrate the plight of general nurses who are not trained to work with psychiatric patients but continue to do so. Findings further accentuate what is already known about the labelling that goes with psychiatric patients and aggression as a resultant effect. Recommendations were made for future research, policy makers, nursing education and practice. / Health Studies / M.A. (Health Studies)

<p dir="ltr">Neurological disorders present a considerable challenge due to their variety and diagnostic complexity especially for older adults. Early prediction of the onset and ongoing assessment of the severity of these disease conditions can allow timely interventions. Currently, most of the assessment tools are time-consuming, costly, and not suitable for use in primary care. To reduce this burden, the present thesis introduces passive digital markers for different disease conditions that can effectively automate the severity assessment and risk prediction from different modalities of electronic health records (EHR). The focus of the first phase of the present study in on developing passive digital markers for the functional assessment of patients suffering from Bipolar disorder and Schizophrenia. The second phase of the study explores different architectures for passive digital markers that can predict patients at risk for dementia. The functional severity PDM uses only a single EHR modality, namely medical notes in order to assess the severity of the functioning of schizophrenia, bipolar type I, or mixed bipolar patients. In this case, the input of is a single medical note from the electronic medical record of the patient. This note is submitted to a hierarchical BERT model which classifies at-risk patients. A hierarchical attention mechanism is adopted because medical notes can exceed the maximum allowed number of tokens by most language models including BERT. The functional severity PDM follows three steps. First, a sentence-level embedding is produced for each sentence in the note using a token-level attention mechanism. Second, an embedding for the entire note is constructed using a sentence-level attention mechanism. Third, the final embedding is classified using a feed-forward neural network which estimates the impairment level of the patient. When used prior to the onset of the disease, this PDM is able to differentiate between severe and moderate functioning levels with an AUC of 76%. Disease-specific severity assessment PDMs are only applicable after the onset of the disease and have AUCs of nearly 85% for schizophrenia and bipolar patients. The dementia risk prediction PDM considers multiple EHR modalities including socio-demographic data, diagnosis codes and medical notes. Moreover, the observation period and prediction horizon are varied for a better understanding of the practical limitations of the model. This PDM is able to identify patients at risk of dementia with AUCs ranging from 70% to 92% as the observation period approaches the index date. The present study introduces methodologies for the automation of important clinical outcomes such as the assessment of the general functioning of psychiatric patients and the prediction of risk for dementia using only routine care data.</p>