Barriers to Effective Implementation of Contingency Management in Outpatient Treatment of Methamphetamine

The objective of this project is to: 1) identify current understanding, attitudes, and beliefs of clinicians at a midsized urban outpatient substance use treatment clinic regarding contingency management (CM) treatment modality; 2) examine how this data contributes to barriers to implementation of contingency management for methamphetamine treatment; and 3) make recommendations to improve implementation strategies. A ten-question survey was developed based on Social Ecological Theory (U.S. Department of Health and Human Services, 2005), and was administered to 31 clinicians. A key informant interview was conducted using theoretical sampling (LoBiondo-Wood & Haber, 2018) of emergent themes. Three major barriers emerged from the data, including characteristics of methamphetamine use disorder, integration of CM into agency process, and lack of client resources. Limitations of the study included a small sample size, and limited representation of agencies. Recommendations include the administration of client interviews to develop client centered, feasible solutions

Comparative Study of Classroom Management Strategies employed by Public and Private School English Language Teachers

This research project was a comparative study of classroom management practices of English language teachers in secondary level public and private sector schools of Lahore. The purpose of the research was to establish the practices and the strategies used by the English language teachers. A total of 200 including 100 public sector and 100 private sector school teachers, teaching English language, were randomly selected for the present research using random sampling techniques. A close-ended questionnaire was developed by the researchers to collect data from the respondents. The researchers personally collected the data. After receiving the data they were entered into the spreadsheet of SPSS version 21.0. Different statistical techniques were used to analyze the data. Mean scores along with standard deviations were calculated in descriptive statistics. In inferential statistics, independent sample t-tests and one-way ANOVA were calculated. There were significant differences in the classroom management strategies used by public sector and private sector English language teachers. Policy recommendations were given for the administration of the schools to encourage EFL teachers to effectively use classroom management strategies

The Ingalls-Thomas Bijections

Given a finite acyclic quiver Q with path algebra kQ, Ingalls and Thomas have exhibited a bijection between the set of Morita equivalence classes of support-tilting modules and the set of thick subcategories of mod kQ and they have collected a large number of further bijections with these sets. We add some additional bijections and show that all these bijections hold for arbitrary hereditary artin algebras. The proofs presented here seem to be of interest also in the special case of the path algebra of a quiver.Comment: This is a modified version of an appendix which was written for the paper "The numbers of support-tilting modules for a Dynkin algebra" (see arXiv:1403.5827v1

How workplace loneliness harms employee well-being:A moderated mediational model

This study investigated the effect of workplace loneliness on work-related subjective well-being by proposing work engagement as an explanatory mechanism in the workplace loneliness—job dissatisfaction relationship. Moreover, the study examines the need to belong as a coping mechanism in the relationship between workplace loneliness and work engagement. Specifically, the study posits that workplace loneliness reduces the positive and fulfilling state of work engagement that in turn increases job dissatisfaction and that this mediation depends on the employee’s level of need to belong. Data were collected from employees (N = 274) working in diverse domestic and multinational organizations in Lahore, Pakistan. Results showed that workplace loneliness reduced the work engagement of lonely individuals that in turn increased their job dissatisfaction. However, the deleterious effect of workplace loneliness on work engagement was weaker for individuals having a higher need to belong. These findings have important implications for organizations wishing to mitigate the harmful effects of workplace loneliness on employees’ subjective well-being.</p

How workplace loneliness harms employee well-being:A moderated mediational model

This study investigated the effect of workplace loneliness on work-related subjective well-being by proposing work engagement as an explanatory mechanism in the workplace loneliness—job dissatisfaction relationship. Moreover, the study examines the need to belong as a coping mechanism in the relationship between workplace loneliness and work engagement. Specifically, the study posits that workplace loneliness reduces the positive and fulfilling state of work engagement that in turn increases job dissatisfaction and that this mediation depends on the employee’s level of need to belong. Data were collected from employees (N = 274) working in diverse domestic and multinational organizations in Lahore, Pakistan. Results showed that workplace loneliness reduced the work engagement of lonely individuals that in turn increased their job dissatisfaction. However, the deleterious effect of workplace loneliness on work engagement was weaker for individuals having a higher need to belong. These findings have important implications for organizations wishing to mitigate the harmful effects of workplace loneliness on employees’ subjective well-being.</p

Semi-supervised sequence classification through change point detection

Sequential sensor data is generated in a wide variety of practical applications. A fundamental challenge involves learning effective classifiers for such sequential data. While deep learning has led to impressive performance gains in recent years in domains such as speech, this has relied on the availability of large datasets of sequences with high-quality labels. In many applications, however, the associated class labels are often extremely limited, with precise labelling/segmentation being too expensive to perform at a high volume. However, large amounts of unlabeled data may still be available. In this paper we propose a novel framework for semi-supervised learning in such contexts. In an unsupervised manner, change point detection methods can be used to identify points within a sequence corresponding to likely class changes. We show that change points provide examples of similar/dissimilar pairs of sequences which, when coupled with labeled, can be used in a semi-supervised classification setting. Leveraging the change points and labeled data, we form examples of similar/dissimilar sequences to train a neural network to learn improved representations for classification. We provide extensive synthetic simulations and show that the learned representations are superior to those learned through an autoencoder and obtain improved results on both simulated and real-world human activity recognition datasets.Comment: 14 pages, 9 figure