Assessing the influence of religion on health behavior

A primary aim of this study was to confirm the factor structure of the Health and Religious Congruency Scale (HARCS), a measure previously developed by the same research team. The HARCS questions directly link religious beliefs/activities to health behaviors. Confirmatory factor analysis (CFA) showed that the current data fit poorly to the factor structure found in the pilot study. Because the current sample was more religiously diverse than the pilot study sample, and could potentially provide a factor structure that better reflects the views of individuals from different religious affiliations, a principal components analysis was conducted on the current data. CFA was then performed on combined data from the pilot study and current investigation. The resulting factor structure had acceptable Goodness of Fit Indices. After eliminating one scale because of highly skewed data and limited utility, and two other scales because of poor test-retest reliability, validity tests were performed on the four remaining HARCS subscales and the total score of the HARCS. Subscale 1 is related to the general influence of religion on drinking and smoking. Hierarchical regression showed that religious variables, drinking and smoking behaviors, age, and an interaction between religion and drinking/smoking accounted for approximately 50% of the variance in the subscale. Subscales 2-4 were related to the impact of religion on eating, physical activity, and weight. Religious variables, health behaviors, and age accounted for small amounts of variance in these scales. In subscales 2-4 few participants endorsed that religion impacted the health behaviors of interest. Overall, results provide further support that from a religious perspective drinking and smoking behaviors are viewed differently than eating and physical activity. The four subscales have adequate reliability, however, the three subscales pertaining to exercise, eating, and weight appear to have little relevancy to the general population. In contrast, subscale 1 appears to have utility with individuals of various religious orientations. For this reason, future studies should consider using subscale 1 without the other subscales. Future studies will be required to determine if the other subscales are valid and useful among select groups such as those participating in faith-based health programs

Panoramic Mapping of Urban Social Sustainability: A 35-Year Bibliometric and Visualization Analysis

In recent years, ensuring social sustainability has been a global concern for sustainable urban development in both the academic arena and sustainability science. Many studies have been conducted in this area, but a bibliometric analysis has not yet been done previously. This study identified research streams and research hotspots in the urban social sustainability field based on a bibliometric analysis from 1985 to 2020, involving 1,623 documents from the Web of Science database. We used two software packages, Bibliometrix (Biblioshiny) and VOSviewer, for performance and science mapping analysis. The result showed that this research field is growing fast in multiple disciplines. In the publication trend analysis, we found significant changes since 2015. Analysis of leading countries and institutions revealed that developed countries are performing better than developing countries in producing publications on urban social sustainability. In the content analysis, we selected 214 documents and found that the survey method was the most used. Additionally, we found that 13.08 percent of papers (28 out of 214) used as many as 21 different theories, where ‘stakeholder theory,’ ‘planning theory,’ ‘theory of urbanism as a way of life,’ and ‘theory of good city form’ were significantly used. The findings of this study can assist researchers and practitioners by providing valuable insights into the research area of urban social sustainability

Panoramic Mapping of Urban Social Sustainability: A 35-Year Bibliometric and Visualization Analysis

In recent years, ensuring social sustainability has been a global concern for sustainable urban development in both the academic arena and sustainability science. Many studies have been conducted in this area, but a bibliometric analysis has not yet been done previously. This study identified research streams and research hotspots in the urban social sustainability field based on a bibliometric analysis from 1985 to 2020, involving 1,623 documents from the Web of Science database. We used two software packages, Bibliometrix (Biblioshiny) and VOSviewer, for performance and science mapping analysis. The result showed that this research field is growing fast in multiple disciplines. In the publication trend analysis, we found significant changes since 2015. Analysis of leading countries and institutions revealed that developed countries are performing better than developing countries in producing publications on urban social sustainability. In the content analysis, we selected 214 documents and found that the survey method was the most used. Additionally, we found that 13.08 percent of papers (28 out of 214) used as many as 21 different theories, where ‘stakeholder theory,’ ‘planning theory,’ ‘theory of urbanism as a way of life,’ and ‘theory of good city form’ were significantly used. The findings of this study can assist researchers and practitioners by providing valuable insights into the research area of urban social sustainability

Investigating the attainment of optimum data quality for EHR Big Data: proposing a new methodological approach

The value derivable from the use of data is continuously increasing since some years. Both commercial and non-commercial organisations have realised the immense benefits that might be derived if all data at their disposal could be analysed and form the basis of decision taking. The technological tools required to produce, capture, store, transmit and analyse huge amounts of data form the background to the development of the phenomenon of Big Data. With Big Data, the aim is to be able to generate value from huge amounts of data, often in non-structured format and produced extremely frequently. However, the potential value derivable depends on general level of governance of data, more precisely on the quality of the data. The field of data quality is well researched for traditional data uses but is still in its infancy for the Big Data context. This dissertation focused on investigating effective methods to enhance data quality for Big Data. The principal deliverable of this research is in the form of a methodological approach which can be used to optimize the level of data quality in the Big Data context. Since data quality is contextual, (that is a non-generalizable field), this research study focuses on applying the methodological approach in one use case, in terms of the Electronic Health Records (EHR). The first main contribution to knowledge of this study systematically investigates which data quality dimensions (DQDs) are most important for EHR Big Data. The two most important dimensions ascertained by the research methods applied in this study are accuracy and completeness. These are two well-known dimensions, and this study confirms that they are also very important for EHR Big Data. The second important contribution to knowledge is an investigation into whether Artificial Intelligence with a special focus upon machine learning could be used in improving the detection of dirty data, focusing on the two data quality dimensions of accuracy and completeness. Regression and clustering algorithms proved to be more adequate for accuracy and completeness related issues respectively, based on the experiments carried out. However, the limits of implementing and using machine learning algorithms for detecting data quality issues for Big Data were also revealed and discussed in this research study. It can safely be deduced from the knowledge derived from this part of the research study that use of machine learning for enhancing data quality issues detection is a promising area but not yet a panacea which automates this entire process. The third important contribution is a proposed guideline to undertake data repairs most efficiently for Big Data; this involved surveying and comparing existing data cleansing algorithms against a prototype developed for data reparation. Weaknesses of existing algorithms are highlighted and are considered as areas of practice which efficient data reparation algorithms must focus upon. Those three important contributions form the nucleus for a new data quality methodological approach which could be used to optimize Big Data quality, as applied in the context of EHR. Some of the activities and techniques discussed through the proposed methodological approach can be transposed to other industries and use cases to a large extent. The proposed data quality methodological approach can be used by practitioners of Big Data Quality who follow a data-driven strategy. As opposed to existing Big Data quality frameworks, the proposed data quality methodological approach has the advantage of being more precise and specific. It gives clear and proven methods to undertake the main identified stages of a Big Data quality lifecycle and therefore can be applied by practitioners in the area. This research study provides some promising results and deliverables. It also paves the way for further research in the area. Technical and technological changes in Big Data is rapidly evolving and future research should be focusing on new representations of Big Data, the real-time streaming aspect, and replicating same research methods used in this current research study but on new technologies to validate current results

Ask Me! Self-reported features of adolescents experiencing neglect or emotional maltreatment: a rapid systematic review

Neglect is often overlooked in adolescence, due in part to assumptions about autonomy and misinterpretation of behaviors being part of normal adolescent development. Emotional maltreatment (abuse or neglect) has a damaging effect throughout the lifespan, but is rarely recognized amongst adolescents. Our review aims to identify features that adolescents experiencing neglect and/ or emotional maltreatment report. METHOD: A rapid review methodology searched 8 databases (1990-2014), supplemented by hand searching journals, and references, identifying 2,568 abstracts. Two independent reviews were undertaken of 279 articles, by trained reviewers, using standardised critical appraisal. Eligible studies: primary studies of children aged 13-17 years, with substantiated neglect and/ or emotional maltreatment, containing self-reported features. RESULTS: 19 publications from 13 studies were included, demonstrating associations between both neglect and emotional maltreatment with internalising features (9 studies) including depression, post traumatic symptomatology and anxiety; emotional maltreatment was associated with suicidal ideation, while neglect was not (1 study); neglect was associated with alcohol related problems (3 studies), substance misuse (2 studies), delinquency for boys (1 study), teenage pregnancy (1 study), and general victimization for girls (1 study), while emotionally maltreated girls reported more externalising symptoms (1 study). Dating violence victimization was associated with neglect and emotional maltreatment (2 studies), while emotional abuse of boys, but not neglect, was associated with dating violence perpetration (1 study), and neither neglect nor emotional maltreatment had an association with low self-esteem (2 studies). Neither neglect nor emotional maltreatment had an effect on school performance (1 study), but neglected boys showed greater school engagement than neglected girls (1 study). CONCLUSIONS: If asked, neglected or emotionally maltreated adolescents describe significant difficulties with their mental health, social relationships, and alcohol or substance misuse. Practitioners working with youths who exhibit these features should recognize the detrimental impact of maltreatment at this developmental stage, and identify whether maltreatment is a contributory factor that should be addressed

Wearables and location tracking technologies for mental-state sensing in outdoor environments

Advances in commercial wearable devices are increasingly facilitating the collection and analysis of everyday physiological data. This paper discusses the theoretical and practical aspects of using such ambulatory devices for the detection of episodic changes in physiological signals as a marker for mental state in outdoor environments. A pilot study was conducted to evaluate the feasibility of utilizing commercial wearables in combination with location tracking technologies. The study measured physiological signals for 15 participants, including heart rate, heart-rate variability, and skin conductance. Participants' signals were recorded during an outdoor walk that was tracked using a GPS logger. The walk was designed to pass through various types of environments including green, blue, and urban spaces as well as a more stressful road crossing. The data that was obtained was used to demonstrate how biosensors information can be contextualized and enriched using location information. Significant episodic changes in physiological signals under real-world conditions were detectable in the stressful road crossing, but not in the other types of environments. The article concludes that despite challenges and limitations of current off-the-shelf wearables, the utilization of these devices offers novel opportunities for evaluating episodic changes in physiological signals as a marker for mental state during everyday activities including in outdoor environments