Navigating the spaces of children’s spiritual experiences: influences of tradition(s), multidisciplinarity and perceptions

ABSTRACT

Children across the world report similar spiritual experiences. Empirical studies suggest that most resonate with the children’s traditions; a finding which should, in theory, afford them a safe spiritual space. However, a number of factors can situate them in a less certain place. The paper uses the metaphors of trees imbued with different shades of light, from the vivid, shimmering and opaque to the invisible, to illustrate the types of spiritual spaces in which children find themselves. Their location is shaped in part by connection or disconnection with traditions, alongside wider cultural forces. Three navigation tools are used to show how children may move between these spaces: the spiritual experience and its relationship with traditions; the influences of multidisciplinary approaches; and children’s perceptions of their experiences. The paper concludes that these metaphors and tools may be a useful way to understand the spiritual spaces in which children find themselves.     

The impact of cognitive interventions on cognitive symptoms in idiopathic Parkinson’s disease: a systematic review

This systematic review addressed efficacy of cognitive stimulation (CS), cognitive training (CT), and cognitive rehabilitation (CR) to improve cognitive functions in Parkinson’s disease (PD) with (PD-MCI) and without mild cognitive impairment (PD-H). Five databases were searched. Twelve CT, four CS, and a combination of CT with CR were found. PD-H benefited from CT or CS compared to active or passive controls in 42.1% of cognitive tests, and in 33.3% of psychological and functional measures. PD-MCI alone, compared with controls, only improved in 6.9% of cognitive measures after CT. PD-H and PD-MCI, alone or together, somehow improved information processing speed, attention, working memory, executive functions, and visual episodic memory. PD-MCI improved better than PD-H in global cognition and planning abilities. The outcomes suggest some efficacy of cognitive interventions in PD. However, small samples, lack of information regarding standardization of interventions, and poor methodological quality limit results validity and prevent firm conclusions.     

Examining multiple feature evaluation and classification methods for improving the diagnosis of Parkinson’s disease

An accurate diagnosis of Parkinson’s disease by specialists involves many neurological, psychological and physical examinations. The specialists investigate a number of symptoms and signs when examining the nervous system conditions of a person. The diagnosis involves reviewing the medical history and genetic factor of the person. The recent diagnosis methodology to Parkinson’s disease relies on voice disorders analysis. This methodology entails extracting feature sets of a recorded person’s voice then utilizing a machine learning technique to identify the healthy and Parkinson’s cases from the voice. This paper attempts to improve the diagnoses of Parkinson’s disease by testing multiple feature evaluation and classification machine learning methods based on the voice disorders analysis. The aim of this paper is to find the optimal solution to the problem by (i) proposing a new Multiple Feature Evaluation Approach (MFEA) of a multi-agent system (ii) implementing five independent classification schemas which are Decision Tree, Naïve Bayes, Neural Network, Random Forests, and Support Vector Machine on the Parkinson’s diagnosis before and after applying the MFEA, and (iii) evaluating the diagnosis accuracy of the results. The methodology of the tests encompasses 10-fold cross-validation to evaluate the learning of methods and track variation in their performance. The test results show that the MFEA of the multi-agent system finds the best set of features and improves the performance of the classifiers. The average rate of improvement in the diagnostic accuracy of the classifiers are Decision Tree 10.51%, Naïve Bayes 15.22%, Neural Network 9.19%, Random Forests 12.75%, and Support Vector Machine 9.13%. These results show that the MFEA makes a significant improvement to the classifiers’ diagnosis results.