Woodwardsheppard3334

This mixed-methods paper describes the development and preliminary validation of the Behavioral Intentions Questionnaire (BIQ), a multi-scale questionnaire developed to assess determinants influencing Puerto Rican adolescents' intentions to engage in abusive behaviors in dating relationships. Items were developed qualitatively, and face and content validity were established by expert and target population judges via semi-structured interviews (n = 48), discussions, and four focus groups (n = 6 each). The questionnaire was pilot tested twice. An initial pilot test was conducted with students aged 13 to 17 from a private alternative education program in San Juan, Puerto Rico (n = 32). A second pilot test was conducted with a sample of students from the same site (n = 22), in addition to students in the same age range from a private school (n = 88) in San Juan. Confirmatory and Exploratory factor analysis was used to determine construct validity and Cronbach's coefficient alpha determined the subscales internal consistency reliability. Correlations between subscales were examined. Thematic content analysis was used to analyze qualitative data. Qualitative data suggested the need to revise or eliminate items and instructions and incorporate a social desirability measure. Factor analyses yielded a unidimensional structure for each subscale and each subscale demonstrated high internal consistency. Preliminary analysis on the factor structure, internal reliability, and validity of the BIQ were encouraging. However, further psychometric testing is needed before this measure can be considered a useful tool for measuring intentions to engage in abusive behaviors in dating relationships.

The online version contains supplementary material available at 10.1007/s10896-021-00341-x.

The online version contains supplementary material available at 10.1007/s10896-021-00341-x.We propose a novel risk measure that is built on comparing high-frequency time-varying volatility and low-frequency return spillover estimates. This measure permits to identify the markets that are epidemic in their complex interdependence. We conjecture that initially a highly volatile market experiences episodes of risk transmission, but only later absorbs risk and becomes an epidemic market. Moreover, we can detect newly emerging 'contagion' in the system. We examine the behaviour of 30 global equity markets and compare spillover measures, which encapsulate many large and small crises episodes. Instead of relying on ex post crisis information, our model identifies crises periods. An important implication of the proposed approach is that highly interrelated markets, such as China, are less likely to transmit a global economic crisis under the current interdependence setting.Studies carried out in different countries correlate social, economic, environmental, and health factors with the number of cases and deaths from COVID-19. However, such studies do not reveal which factors make one country more exposed to COVID-19 than other. Based on the composite indicators approach, this research identifies the factors that most impact the number of cases and deaths of COVID-19 worldwide and measures countries' exposure to COVID-19. Three composite indicators of exposure to COVID-19 were constructed through Principal Component Analysis, Simple Additive Weighting, and k-means clustering. The number of cases and deaths from COVID-19 is strongly correlated ( R  > 0.60) with composite indicator scores and moderately concordant ( K  > 0.4) with country clusters. Factors directly or indirectly associated with the age of the population are the ones that most expose countries to COVID-19. The population of countries most exposed to COVID-19 is 12 years older on average. The proportion of the elderly population in these countries is at least twice that of countries less exposed to COVID-19. Factors that can increase the population's life expectancy, such as Gross Domestic Product per capita and the Human Development Index, are four times and 1.3 times higher in more exposed countries to COVID-19. Providing better living conditions increases both the population's life expectancy and the country's exposure to COVID-19.Since digitalisation alters occupational task profiles via automation processes, job quality is also likely to be affected. While existing literature mainly focusses on objective job quality, this study asks if and how digitalisation is associated with employees' subjective job quality in the second half of working life in Germany. Analyses are based on the German Ageing Survey 2014. Our sample includes n = 1541 employees aged 40-65 years who are subject to social insurance contributions. Subjective job quality is operationalised with regards to job satisfaction and perceived occupational stress in general, and ten aspects of job quality in detail. Digitalisation is approximated by substitution potentials of occupations. We control the association for compositional effects in the workforce, as well as for the moderating effect of perceived job insecurity. The results indicate that digitalisation is predominantly beneficial but also unfavourable in a few other respects for employees' subjective job quality. The higher the degree of digitalisation, the higher is the employee's general job satisfaction on average; for general perceived occupational stress, we find no significant association. Regarding single aspects of subjective job quality, employees working in more digitalised occupations are found to report on average higher satisfaction with working hours and earnings, and lower levels of stress due to tight schedules. However, they also report higher levels of stress due to negative environmental factors.

The online version contains supplementary material available at 10.1007/s11205-021-02854-w.

The online version contains supplementary material available at 10.1007/s11205-021-02854-w.The subcortical structures of the brain are relevant for many neurodegenerative diseases like Huntington's disease (HD). Quantitative segmentation of these structures from magnetic resonance images (MRIs) has been studied in clinical and neuroimaging research. Recently, convolutional neural networks (CNNs) have been successfully used for many medical image analysis tasks, including subcortical segmentation. In this work, we propose a 2-stage cascaded 3D subcortical segmentation framework, with the same 3D CNN architecture for both stages. Attention gates, residual blocks and output adding are used in our proposed 3D CNN. In the first stage, we apply our model to downsampled images to output a coarse segmentation. Next, we crop the extended subcortical region from the original image based on this coarse segmentation, and we input the cropped region to the second CNN to obtain the final segmentation. Left and right pairs of thalamus, caudate, pallidum and putamen are considered in our segmentation. We use the Dice coefficient as our metric and evaluate our method on two datasets the publicly available IBSR dataset and a subset of the PREDICT-HD database, which includes healthy controls and HD subjects. We train our models on only healthy control subjects and test on both healthy controls and HD subjects to examine model generalizability. Compared with the state-of-the-art methods, our method has the highest mean Dice score on all considered subcortical structures (except the thalamus on IBSR), with more pronounced improvement for HD subjects. This suggests that our method may have better ability to segment MRIs of subjects with neurodegenerative disease.Longitudinal information is important for monitoring the progression of neurodegenerative diseases, such as Huntington's disease (HD). Specifically, longitudinal magnetic resonance imaging (MRI) studies may allow the discovery of subtle intra-subject changes over time that may otherwise go undetected because of inter-subject variability. For HD patients, the primary imaging-based marker of disease progression is the atrophy of subcortical structures, mainly the caudate and putamen. learn more To better understand the course of subcortical atrophy in HD and its correlation with clinical outcome measures, highly accurate segmentation is important. In recent years, subcortical segmentation methods have moved towards deep learning, given the state-of-the-art accuracy and computational efficiency provided by these models. However, these methods are not designed for longitudinal analysis, but rather treat each time point as an independent sample, discarding the longitudinal structure of the data. In this paper, we propose a deep learning based subcortical segmentation method that takes into account this longitudinal information. Our method takes a longitudinal pair of 3D MRIs as input, and jointly computes the corresponding segmentations. We use bi-directional convolutional long short-term memory (C-LSTM) blocks in our model to leverage the longitudinal information between scans. We test our method on the PREDICT-HD dataset and use the Dice coefficient, average surface distance and 95-percent Hausdorff distance as our evaluation metrics. Compared to cross-sectional segmentation, we improve the overall accuracy of segmentation, and our method has more consistent performance across time points. Furthermore, our method identifies a stronger correlation between subcortical volume loss and decline in the total motor score, an important clinical outcome measure for HD.Difficulty in validating accuracy remains a substantial setback in the field of surface-based cortical thickness (CT) measurement due to the lack of experimental validation against ground truth. Although methods have been developed to create synthetic datasets for this purpose, none provide a robust mechanism for measuring exact thickness changes with surface-based approaches. This work presents a registration-based technique for inducing synthetic cortical atrophy to create a longitudinal, ground truth dataset specifically designed for accuracy validation of surface-based CT measurements. Across the entire brain, we show our method can induce up to between 0.6 and 2.6 mm of localized cortical atrophy in a given gyrus depending on the region's original thickness. By calculating the image deformation to induce this atrophy at 400% of the original resolution in each direction, we can induce a sub-voxel resolution amount of atrophy while minimizing partial volume effects. We also show that our method can be extended beyond application to CT measurements for the accuracy validation of longitudinal cortical segmentation and surface reconstruction pipelines when measuring accuracy against cortical landmarks. Importantly, our method relies exclusively on publicly available software and datasets.The public hearing is a vital method to obtain citizen participation and information gathering for urban policy decision making. However, the COVID-19 pandemic has caused local planning departments around the nation to rethink their strategy, especially when many citizens are unable to use many of the new strategies because of the rural digital divide. While fully online meetings would be ideal for the current situation, the reality is that the lack of Internet and technology severely limits public participation among certain populations and in certain regions. This paper analyzed nine counties in the state of Florida, USA, in terms of population, COVID-19 cases, Internet broadband availability, and public hearing strategies, as well as survey data regarding public hearings, to produce best practices for holding a public hearing during the pandemic. A hybrid public hearing approach is the most effective method given the circumstances, and best practices and future approaches are provided and discussed to help bridge the digital divide.