19,829 research outputs found

    Implementing Health Impact Assessment as a Required Component of Government Policymaking: A Multi-Level Exploration of the Determinants of Healthy Public Policy

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    It is widely understood that the public policies of ‘non-health’ government sectors have greater impacts on population health than those of the traditional healthcare realm. Health Impact Assessment (HIA) is a decision support tool that identifies and promotes the health benefits of policies while also mitigating their unintended negative consequences. Despite numerous calls to do so, the Ontario government has yet to implement HIA as a required component of policy development. This dissertation therefore sought to identify the contexts and factors that may both enable and impede HIA use at the sub-national (i.e., provincial, territorial, or state) government level. The three integrated articles of this dissertation provide insights into specific aspects of the policy process as they relate to HIA. Chapter one details a case study of purposive information-seeking among public servants within Ontario’s Ministry of Education (MOE). Situated within Ontario’s Ministry of Health (MOH), chapter two presents a case study of policy collaboration between health and ‘non-health’ ministries. Finally, chapter three details a framework analysis of the political factors supporting health impact tool use in two sub-national jurisdictions – namely, Québec and South Australia. MOE respondents (N=9) identified four components of policymaking ‘due diligence’, including evidence retrieval, consultation and collaboration, referencing, and risk analysis. As prospective HIA users, they also confirmed that information is not routinely sought to mitigate the potential negative health impacts of education-based policies. MOH respondents (N=8) identified the bureaucratic hierarchy as the brokering mechanism for inter-ministerial policy development. As prospective HIA stewards, they also confirmed that the ministry does not proactively flag the potential negative health impacts of non-health sector policies. Finally, ‘lessons learned’ from case articles specific to Québec (n=12) and South Australia (n=17) identified the political factors supporting tool use at different stages of the policy cycle, including agenda setting (‘policy elites’ and ‘political culture’), implementation (‘jurisdiction’), and sustained implementation (‘institutional power’). This work provides important insights into ‘real life’ policymaking. By highlighting existing facilitators of and barriers to HIA use, the findings offer a useful starting point from which proponents may tailor context-specific strategies to sustainably implement HIA at the sub-national government level

    Lift EVERY Voice and Sing: An Intersectional Qualitative Study Examining the Experiences of Lesbian, Gay, Bisexual, and Queer Faculty and Administrators at Historically Black Colleges and Universities

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    While there is minimal literature that address the experiences of lesbian, gay, bisexual, and trans* identified students at Historically Black Colleges and Universities (HBCUs), the experiences of Black, queer faculty and administrators at HBCUs has not been studied. This intersectional qualitative research study focused on the experiences of lesbian, gay, bisexual, and queer identified faculty and administrators who work at HBCUs. By investigating the intersections of religion, race, gender, and sexuality within a predominantly Black institution, this study aims to enhance diversity, equity, and inclusion efforts at HBCUs by sharing the experiences of the LGBQ faculty and administrators that previously or currently work at an HBCU as a full-time employee. The research questions that guided this study were 1) How have LGBQ faculty and staff negotiated/navigated their careers at HBCUs? and 2) How do LGBQ faculty and staff at HBCUs influence cultural (relating to LGBQ inclusion) change at the organizational level? The main theoretical framework used was intersectionality and it shaped the chosen methodology and methods. The Politics of Respectability was the second theoretical framework used to describe the intra-racial tensions within the Black/African American community. The study included 60-120 minute interviews with 12 participants. Using intersectionality as a guide, the data were coded and utilized for thematic analysis. Then, an ethnodramatic performance engages readers. The goals of this study were to encourage policy changes, promote inclusivity for LGBQ employees at HBCUs, and provide an expansion to the body of literature in the field pertaining to the experiences of LGBQ faculty and administrators in higher education

    ENHANCING STUDENT ENGAGEMENT, TEACHER SELF-EFFICACY, AND PRINCIPAL LEADERSHIP SKILLS THROUGH MORNING MEETING IN AN ONLINE LEARNING ENVIRONMENT

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    This study examined the experiences of educators in a small, rural elementary school who provided live instruction in an online setting during the COVID-19 pandemic. The scholarly practitioner collaborated with inquiry partners to enhance student engagement, teacher self-efficacy, and principal leadership skills by implementing Morning Meeting, a social and emotional learning program from Responsive Classroom®, when students participated in remote online learning. The scholarly practitioner used over four decades of research about efficacy and identified leadership strategies and approaches that assisted in building individual and collective teacher efficacy so that teachers could effectively engage students. Behavioral, emotional, and cognitive engagement were identified in research and used by teachers to determine the quality of participation in Morning Meeting. Teachers took daily and weekly attendance to measure engagement, and the scholarly practitioner facilitated team meetings with groups of teachers to compile comments and statements regarding student engagement. These statements were coded using pre-selected codes based on research about types of student engagement. The scholarly practitioner facilitated the administration of a pre-study and post-study Teacher Self-Efficacy Scale so that individual, grade-span, and full-school efficacy data could be compiled. In addition, the scholarly practitioner held team meetings with the teachers to compile comments and categorize those statements into four areas: job accomplishment, skill development, social interaction, and coping with job stress. These four areas were also coded using the four categories described on the Teacher Self-Efficacy Scale. The scholarly practitioner also maintained a journal using a self-reflection tool about the lived experiences before, during, and after the study. The emphasis on this journal was about the development and growth of leadership skills, and the categories were pre-coded using Bernard Bass’s categories of transformational leadership: individualized consideration, inspirational motivation, idealized influence, and intellectual stimulation. Student engagement increased throughout the study, and 77 percent of students were fully engaged during the study. Teachers expressed an increase in collective efficacy at the conclusion of the study, and six of the eight teachers reported individual increases in efficacy. The scholarly practitioner’s use of differentiation within the context of transformational leadership was observed most frequently in the study

    Image classification over unknown and anomalous domains

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    A longstanding goal in computer vision research is to develop methods that are simultaneously applicable to a broad range of prediction problems. In contrast to this, models often perform best when they are specialized to some task or data type. This thesis investigates the challenges of learning models that generalize well over multiple unknown or anomalous modes and domains in data, and presents new solutions for learning robustly in this setting. Initial investigations focus on normalization for distributions that contain multiple sources (e.g. images in different styles like cartoons or photos). Experiments demonstrate the extent to which existing modules, batch normalization in particular, struggle with such heterogeneous data, and a new solution is proposed that can better handle data from multiple visual modes, using differing sample statistics for each. While ideas to counter the overspecialization of models have been formulated in sub-disciplines of transfer learning, e.g. multi-domain and multi-task learning, these usually rely on the existence of meta information, such as task or domain labels. Relaxing this assumption gives rise to a new transfer learning setting, called latent domain learning in this thesis, in which training and inference are carried out over data from multiple visual domains, without domain-level annotations. Customized solutions are required for this, as the performance of standard models degrades: a new data augmentation technique that interpolates between latent domains in an unsupervised way is presented, alongside a dedicated module that sparsely accounts for hidden domains in data, without requiring domain labels to do so. In addition, the thesis studies the problem of classifying previously unseen or anomalous modes in data, a fundamental problem in one-class learning, and anomaly detection in particular. While recent ideas have been focused on developing self-supervised solutions for the one-class setting, in this thesis new methods based on transfer learning are formulated. Extensive experimental evidence demonstrates that a transfer-based perspective benefits new problems that have recently been proposed in anomaly detection literature, in particular challenging semantic detection tasks

    Detection of Hyperpartisan news articles using natural language processing techniques

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    Yellow journalism has increased the spread of hyperpartisan news on the internet. It is very difficult for online news article readers to distinguish hyperpartisan news articles from mainstream news articles. There is a need for an automated model that can detect hyperpartisan news on the internet and tag them as hyperpartisan so that it is very easy for readers to avoid that news. A hyperpartisan news detection article was developed by using three different natural language processing techniques named BERT, ELMo, and Word2vec. This research used the bi-article dataset published at SEMEVAL-2019. The ELMo word embeddings which are trained on a Random forest classifier has got an accuracy of 0.88, which is much better than other state of art models. The BERT and Word2vec models have got the same accuracy of 0.83. This research tried different sentence input lengths to BERT and proved that BERT can extract context from local words. Evidenced from the described ML models, this study will assist the governments, news’ readers, and other political stakeholders to detect any hyperpartisan news, and also helps policy to track, and regulate, misinformation about the political parties and their leaders

    Accountability and Responsiveness in the Covid-19 Response in Bangladesh

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    This research briefing summarises priority areas for future research and stakeholders with whom to engage as identified in the scoping paper "Accountability and Responsiveness in the Covid-19 Response in Bangladesh" by Shuvra Chowdhury, Department of Public Administration, University of Rajshahi and Naomi Hossain, Accountability Research Center, School of International Service, American University. The paper and this briefing were commissioned for the Covid-19 Learning, Evidence and Research Programme in Bangladesh (CLEAR). CLEAR aims to build a consortium of research partners to deliver policy-relevant research and evidence for Bangladesh to support the Covid-19 response and inform preparation for future shocks.Foreign, Commonwealth and Development Office (FCDO) Banglades

    Unraveling the effect of sex on human genetic architecture

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    Sex is arguably the most important differentiating characteristic in most mammalian species, separating populations into different groups, with varying behaviors, morphologies, and physiologies based on their complement of sex chromosomes, amongst other factors. In humans, despite males and females sharing nearly identical genomes, there are differences between the sexes in complex traits and in the risk of a wide array of diseases. Sex provides the genome with a distinct hormonal milieu, differential gene expression, and environmental pressures arising from gender societal roles. This thus poses the possibility of observing gene by sex (GxS) interactions between the sexes that may contribute to some of the phenotypic differences observed. In recent years, there has been growing evidence of GxS, with common genetic variation presenting different effects on males and females. These studies have however been limited in regards to the number of traits studied and/or statistical power. Understanding sex differences in genetic architecture is of great importance as this could lead to improved understanding of potential differences in underlying biological pathways and disease etiology between the sexes and in turn help inform personalised treatments and precision medicine. In this thesis we provide insights into both the scope and mechanism of GxS across the genome of circa 450,000 individuals of European ancestry and 530 complex traits in the UK Biobank. We found small yet widespread differences in genetic architecture across traits through the calculation of sex-specific heritability, genetic correlations, and sex-stratified genome-wide association studies (GWAS). We further investigated whether sex-agnostic (non-stratified) efforts could potentially be missing information of interest, including sex-specific trait-relevant loci and increased phenotype prediction accuracies. Finally, we studied the potential functional role of sex differences in genetic architecture through sex biased expression quantitative trait loci (eQTL) and gene-level analyses. Overall, this study marks a broad examination of the genetics of sex differences. Our findings parallel previous reports, suggesting the presence of sexual genetic heterogeneity across complex traits of generally modest magnitude. Furthermore, our results suggest the need to consider sex-stratified analyses in future studies in order to shed light into possible sex-specific molecular mechanisms
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