Exploring how the social model of disability can be re-invigorated for autism: in response to Jonathan Levitt

Levitt argues that the social model of disability needs to be re-invigorated, potentially by adapting the tool for separate countries. The social model has been successfully applied for some disabled groups in the United Kingdom. However, the social model is not implemented for neurodivergent labels such as autism, through the negative language of autism, causing severe problems for autistic individuals’ daily lives. The social model can be re-invigorated for autism, removing social barriers by changing non-autistic people’s attitudes towards autism through ensuring positive language of autism, preventing the categorisation of autism and fully enacting The Autism Act 2009 and The Equality Act 2010.     

The relationship between the community participation of disabled youth and socioeconomic factors: mixed-methods approach in rural Sri Lanka

Mixed methods were applied to examine the association of community participation by disabled youth with socioeconomic factors in rural Sri Lanka. There was a significant association between community participation and socioeconomic factors in the quantitative survey (n = 116): participants living in socioeconomically disadvantaged conditions had lower levels of community participation than those living without these disadvantages. Consistent with the findings of the survey, previous educational experiences, household economic conditions, and perceived resource information were common themes in the qualitative study (n = 26). The dynamics of these factors, including marginalisation of disabled youth within the household, were also observed.     

Employee communication,job engagement,and organizational commitment: A study of members of the Millennial Generation

The purpose of this study was to explore the relationship between job engagement and two key components of employee-organization relationships (EOR). Findings from a survey of members of the Millennial Generation (N = 539) in the United States indicate that job engagement mediates the relationship between employee communication and organizational commitment. It is concluded that when employees are engaged in their work, their commitment to the organization is strengthened and the likelihood of them leaving the organization decreases. Furthermore, an argument is made in light of the study’s findings that engagement and commitment work in concert to strengthen EORs overall. To foster engagement, organizations should remove obstacles to internal information flow and provide ongoing feedback to employees about individual and organizational issues.     

Burnout,coping and suicidal ideation: An application and extension of the job demand-control-support model

Suicidal tendencies are typically considered related to individual traits or predispositions, but some occupations have significantly higher rates than other occupations and the general population. This article proposes that stressful work conditions may be important in understanding occupational variations in suicidal tendencies. It explores the links between work-related factors and burnout, and suicidal thoughts among veterinarians. Burnout appears to be an important mediator in understanding veterinarians’ suicidal tendencies; work conditions that are emotionally exhausting for veterinarians may foster suicidal thoughts. Individual coping strategies are also explored. Emotion-focused strategies of avoidance and alcohol consumption not only exacerbate burnout and suicidal thoughts but also amplify the harmful effects of some job demands. Active problem solving is not as beneficial as expected, but a supportive work environment is a valuable coping resource. Organizational interventions and coping resources effective in reducing burnout may also reduce the risk of suicide for those in service occupations.     

Investment model predictions of workplace ostracism on K–12 teachers’ commitment to their schools and the profession of teaching

The purpose of this study was to determine how the ostracism of K-12 teachers influences their commitment to their schools and commitment to the teaching profession. The investment model was used to situate ostracism as a predictor of teacher commitment. Participants were 200 full-time K-12 teachers who completed a survey assessing their experiences with ostracism at work and investment model variables (i.e., investments, quality of alternatives, satisfaction, and commitment). Results confirmed investment model predictions with teachers’ investments, quality of alternatives, and satisfaction predicting their commitment to their schools and profession. Results of mediation models also demonstrated that controlling for teachers’ investments and quality of alternatives, ostracism predicted commitment indirectly through its effect on satisfaction. This study revealed that teacher commitment is explained by investment model predictions, but after controlling for those predictions, is further explained by ostracism from teacher colleagues.     

Monitoring the parameter vector of regression models with time-to-event response in phase II processes

In most of the existing specialized literature, monitoring regression models are a special case of profile monitoring. However, not every regression model always represents appropriately a profile data structure. This is clearly the case of the Weibull regression model (WRM) with common shape parameter γ. Even though it might be thought that existing methodologies (especially likelihood-ratio (LRT)-based methods) for monitoring generalized linear profiles can also be successfully applied to monitoring regression models with time-to-event response, it will be shown in this paper that those methodologies work fairly acceptable just for data structures with 1000 observations at least approximately. It was found out that some corrections, often referred to as Bartlett's adjustments, are needed to be implemented in order to improve the accuracy of using the asymptotic distributional properties of the LRT statistic for carrying out the monitoring of WRM with relatively small and moderate dimensions of the available datasets. Simulation studies suggest that the use of the aforementioned corrections make the resulting charts work quite acceptable when available data structures contain 30 observations at least. Detection abilities of the proposed schemes improve as dataset dimension increases.     

A Note on High-Dimensional Linear Regression With Interactions

The problem of interaction selection in high-dimensional data analysis has recently received much attention. This note aims to address and clarify several fundamental issues in interaction selection for linear regression models, especially when the input dimension p is much larger than the sample size n. We first discuss how to give a formal definition of “importance” for main and interaction effects. Then we focus on two-stage methods, which are computationally attractive for high-dimensional data analysis but thus far have been regarded as heuristic. We revisit the counterexample of Turlach and provide new insight to justify two-stage methods from the theoretical perspective. In the end, we suggest new strategies for interaction selection under the marginality principle and provide some simulation results.     

Computationally and statistically efficient model fitting techniques

A significant challenge in fitting metamodels of large-scale simulations with sufficient accuracy is in the computational time required for rigorous statistical validation. This paper addresses the statistical computation issues associated with the Bootstrap and modified PRESS statistic, which yield key metrics for error measurements in metamodelling validation. Experimentation is performed on different programming languages, namely, MATLAB, R, and Python, and implemented on different computing architectures including traditional multicore personal computers and high-power clusters with parallel computing capabilities. This study yields insight into the effect that programming languages and computing architecture have on the computational time for simulation metamodel validation. The experimentation is performed across two scenarios with varying complexity.     

Quantile regression for robust estimation and variable selection in partially linear varying-coefficient models

In this paper, we develop a new estimation procedure based on quantile regression for semiparametric partially linear varying-coefficient models. The proposed estimation approach is empirically shown to be much more efficient than the popular least squares estimation method for non-normal error distributions, and almost not lose any efficiency for normal errors. Asymptotic normalities of the proposed estimators for both the parametric and nonparametric parts are established. To achieve sparsity when there exist irrelevant variables in the model, two variable selection procedures based on adaptive penalty are developed to select important parametric covariates as well as significant nonparametric functions. Moreover, both these two variable selection procedures are demonstrated to enjoy the oracle property under some regularity conditions. Some Monte Carlo simulations are conducted to assess the finite sample performance of the proposed estimators, and a real-data example is used to illustrate the application of the proposed methods.