Central catheter dressing in a simulator: the effects of tutor's assistance or self-learning tutorial

Aims: to compare the performance of undergraduate students concerning semi-implanted central venous catheter dressing in a simulator, with the assistance of a tutor or of a self-learning tutorial. Method: Randomized controlled trial. The sample consisted of 35 undergraduate nursing students, who were divided into two groups after attending an open dialogue presentation class and watching a video. One group undertook the procedure practice with a tutor and the other with the assistance of a self-learning tutorial. Results: in relation to cognitive knowledge, the two groups had lower performance in the pre-test than in the post-test. The group that received assistance from a tutor performed better in the practical assessment. Conclusion: the simulation undertaken with the assistance of a tutor showed to be the most effective learning strategy when compared to the simulation using a self-learning tutorial. Advances in nursing simulation technology are of upmost importance and the role of the tutor in the learning process should be highlighted, taking into consideration the role this professional plays in knowledge acquisition and in the development of critical-reflexive thoughts and attitudes. (ClinicalTrials.gov Identifier: NCT 01614314).

Correlates of change in self-perceived oral health among older adults in Brazil Findings from the Health, Well-Being and Aging Study

Background. Identifying changes in the oral health status of older populations, and their predictors and explanations, is necessary for public health planning. The authors assessed patterns of change in oral health-related quality of life in a large cohort of older adults in Brazil during a five-year period and evaluated associations between baseline characteristics and those changes. Methods. The sample consisted of 747 older people enrolled in a Brazilian cohort study called the Health, Well-Being and Aging (Saude, Bem-estar e Envelhecimento [SABE]) Study. Trained examiners measured participants' self-perceived oral health by using the General Oral Health Assessment Index (GOHAI). The authors calculated changes in the overall GOHAI score and in the scores for each of the GOHAI's three dimensions individually by subtracting the baseline score from the score at follow-up. A positive difference indicated improvement in oral health, a negative difference indicated a decline and a difference of zero indicated no change. Results. The authors found that 48.56 percent of the participants experienced a decline in oral health and 33.48 percent experienced an improvement. Participants with 16 or more missing teeth and eight or more years of education were more likely to have an improvement in total GOHAI score. Deterioration was more likely to occur among those with two or more diseases. Improvement and decline in GOHAI functional scores were related to the number of missing teeth. The authors found no significant model for the change in the psychosocial score, and Self-rated general health was the only variable related to both improvement and decline in pain or discomfort scores. Conclusions. The authors observed a bidirectional change in self-perceived oral health, with deterioration predominating. The strongest predictor of improvement in the total GOHAI score was the number of missing teeth, whereas the number of diseases was the strongest predictor of deterioration. Clinical Implications. Dental professionals and policymakers need to know the directions of change in older adults' oral health to establish treatment priorities and evaluate the impact of services directed at this population.

Non-Dominated Sorting Genetic Algorithm Based on Reinforcement Learning to Optimization of Broad-Band Reflector Antennas Satellite

This paper aims to provide an improved NSGA-II (Non-Dominated Sorting Genetic Algorithm-version II) which incorporates a parameter-free self-tuning approach by reinforcement learning technique, called Non-Dominated Sorting Genetic Algorithm Based on Reinforcement Learning (NSGA-RL). The proposed method is particularly compared with the classical NSGA-II when applied to a satellite coverage problem. Furthermore, not only the optimization results are compared with results obtained by other multiobjective optimization methods, but also guarantee the advantage of no time-spending and complex parameter tuning.