Spirituality is known to play a significant role in patients' well-being and quality of life. Responding to patients' spiritual needs is considered to be an essential element of high quality medical care. Consequently, it seems logical that there is a professional requirement for nurses to achieve competence in the delivery of spiritual care. This study aims to examine the impact of nurses' spiritual well-being on patients' spiritual care. A total of 210 nurses working in critical care units completed Basic Psychological Needs questionnaire and Spiritual Care Competence Scale. 5.8% of nurses provided spiritual care at a poor level; 53.4% at an optimal level; and 39.8% at a highly desirable level. There were negative significant relations between the average scores of spiritual well-being with: age (p<0.04); and clinical experience (p<0.02). There were positive significant relations between the receipt of training by nurses in the principles of spirituality with: the level of spiritual well-being (p<0.003); and the level of spiritual care (p<0.02). Overall, a significant relationship was observed between spiritual well-being and spiritual care (p<0.001). The study has demonstrated that there was a positive relationship between nurses' spiritual well-being and the provision of spiritual care. Implementation of strategies that might develop spiritual well-being in nurses would be of great benefit in catering for the spiritual needs of patients.
Road traffic injuries (RTIs) are realised as a main cause of public health problems at global, regional and national levels. Therefore, prediction of road traffic death rate will be helpful in its management. Based on this fact, we used an artificial neural network model optimised through Genetic algorithm to predict mortality. In this study, a five-fold cross-validation procedure on a data set containing total of 178 countries was used to verify the performance of models. The best-fit model was selected according to the root mean square errors (RMSE). Genetic algorithm, as a powerful model which has not been introduced in prediction of mortality to this extent in previous studies, showed high performance. The lowest RMSE obtained was 0.0808. Such satisfactory results could be attributed to the use of Genetic algorithm as a powerful optimiser which selects the best input feature set to be fed into the neural networks. Seven factors have been known as the most effective factors on the road traffic mortality rate by high accuracy. The gained results displayed that our model is very promising and may play a useful role in developing a better method for assessing the influence of road traffic mortality risk factors.
This study aimed to compile existing evidence about the proposed relationships among variables at three stages of the model of therapeutic engagement (MTE): patient intention to engage in cardiac rehabilitation (CR), CR initiation, and sustained engagement. This model has not been tested in any rehabilitation setting. Therefore, this systematic literature review is key to future research and application of MTE to predict and enhance patient engagement in CR. Model-centric systematic literature reviews have been conducted for each stage of the MTE. A coherent approach to understanding and monitoring the process of patient engagement in CR is absent. Few relevant studies included in the model-centric reviews met the criteria: eight in stage 1, four in stage 2, and six in stage 3 of the MTE. In total, the tenets of the MTE were supported in patient intention to engage in CR. However, there was less evidence quantifying the proposed relationships among variables that impact on CR initiation and sustained engagement. There is a scarcity of research examining rehabilitation engagement in depth to better understand the complicated process contributing to behavioural outcomes. No decision-support models currently exist to alert patients and healthcare provider to the factors that influence non-engagement.
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