Registered nurses' perceptions and experience of working in aged care: A qualitative approach.

AIM: To understand and report on the perceptions and experiences of registered nurses in the aged care sector. DESIGN: An exploratory qualitative study. METHODS: Semi-structured telephone interviews were utilised as the primary data collection method. Fifteen registered nurses were interviewed. All interviews were recorded, transcribed verbatim and analysed using conventional content analysis. Participants were quoted verbatim to ensure authenticity. RESULTS: The results indicated a demand for increased administrative and staffing support in the aged care workplace. Poor morale and unethical practices contributed to negative perceptions and attitudes among nurses towards aged care. Managing and communicating with older people was reported as challenging, which impacts nursing staff recruitment and retention. Future work is needed to ensure that outstanding clinical role models and leadership support nursing staff recruitment and retention. Incorporating aged care content into the nursing curriculum and providing professional development opportunities to aged care professionals would be the foundation towards solutions, as the study primarily explored nurses' perspectives.

Developing an ontology of non-pharmacological treatment for emotional and mood disturbances in dementia.

Emotional and mood disturbances are common in people with dementia. Non-pharmacological interventions are beneficial for managing these disturbances. However, effectively applying these interventions, particularly in the person-centred approach, is a complex and knowledge-intensive task. Healthcare professionals need the assistance of tools to obtain all relevant information that is often buried in a vast amount of clinical data to form a holistic understanding of the person for successfully applying non-pharmacological interventions. A machine-readable knowledge model, e.g., ontology, can codify the research evidence to underpin these tools. For the first time, this study aims to develop an ontology entitled Dementia-Related Emotional And Mood Disturbance Non-Pharmacological Treatment Ontology (DREAMDNPTO). DREAMDNPTO consists of 1258 unique classes (concepts) and 70 object properties that represent relationships between these classes. It meets the requirements and quality standards for biomedical ontology. As DREAMDNPTO provides a computerisable semantic representation of knowledge specific to non-pharmacological treatment for emotional and mood disturbances in dementia, it will facilitate the application of machine learning to this particular and important health domain of emotional and mood disturbance management for people with dementia.