The impact of a simulated intervention on attitudes of undergraduate nursing and medical students towards end of life care provision

Background: The concerns of undergraduate nursing and medical students’ regarding end of life care are well documented. Many report feelings of emotional distress, anxiety and a lack of preparation to provide care to patients at end of life and their families. Evidence suggests that increased exposure to patients who are dying and their families can improve attitudes toward end of life care. In the absence of such clinical exposure, simulation provides experiential learning with outcomes comparable to that of clinical practice. The aim of this study was therefore to assess the impact of a simulated intervention on the attitudes of undergraduate nursing and medical students towards end of life care.
Methods: A pilot quasi-experimental, pretest-posttest design. Attitudes towards end of life care were measured using the Frommelt Attitudes Towards Care of the Dying Part B Scale which was administered pre and post a simulated clinical scenario. 19 undergraduate nursing and medical students were recruited from one large Higher Education Institution in the United Kingdom.
Results: The results of this pilot study confirm that a simulated end of life care intervention has a positive impact on the attitudes of undergraduate nursing and medical students towards end of life care (p < 0.001).
Conclusions: Active, experiential learning in the form of simulation teaching helps improve attitudes of undergraduate nursing and medical students towards end of life. In the absence of clinical exposure, simulation is a viable alternative to help prepare students for their professional role regarding end of life care.

Time series wind power forecasting based on variant Gaussian process and TLBO

Due to the variability and stochastic nature of wind power system, accurate wind power forecasting has an important role in developing reliable and economic power system operation and control strategies. As wind variability is stochastic, Gaussian Process regression has recently been introduced to capture the randomness of wind energy. However, the disadvantages of Gaussian Process regression include its computation complexity and incapability to adapt to time varying time-series systems. A variant Gaussian Process for time series forecasting is introduced in this study to address these issues. This new method is shown to be capable of reducing computational complexity and increasing prediction accuracy. It is further proved that the forecasting result converges as the number of available data approaches innite. Further, a teaching learning based optimization (TLBO) method is used to train the model and to accelerate
the learning rate. The proposed modelling and optimization method is applied to forecast both the wind power generation of Ireland and that from a single wind farm to show the eectiveness of the proposed method.