This research was conducted at Tsakane, Kwa-Thema, and Duduza, Ekurhuleni Metropolitan Municipality of Gauteng Province in South Africa. The objective of the study was to determine the effectiveness of social protection as a way of alleviating poverty in the study areas. The study was expected to contribute to the body of knowledge in social protection services as a way of alleviating poverty in the study areas. The study attempted to contribute to answers to the following research questions: (i) What are the socio-economic characteristics of the recipients of social protection measures in the three areas of study? (ii) What are the perceptions of the respondents about the South African government’s social protection in their areas? (iii) What are the factors that influence the effectiveness of social protection? Stratified random sampling with a proportional representation method was employed to select 200 respondents. The data collection tool used was simple closed-ended questionnaires. Interviews were conducted face-to-face with respondents. Statistical Package for Social Sciences (SPSS) version 21 of 2012 was used to analyse the data. Both descriptive statistics and binary logistic regression were employed. The results of the analysis revealed that the significant variables that had an effect on social protection were: the location of the respondents; their gender; their level of education; the type of dwelling of the respondents; and their income outside farming. The study recommends that the significant variables that had an effect on social protection be considered when measures of social protection measures are implemented / College of Agriculture and Environmental Sciences / M. Sc. (Agriculture)

Please read the abstract (Summary) in the 00front part of this document / Dissertation (MA (HMS))--University of Pretoria, 2002. / Biokinetics, Sport and Leisure Sciences / unrestricted

This cross-sectional, exploratory study aimed to determine the prevalence and characteristics of self-harming behaviours among a sample of South African university students (N = 603), as well as the relationship between deliberate self-harm (DSH) and symptoms of depression and anxiety. A battery of instruments, including the Beck Depression Inventory (BDI-II), State-Trait Anxiety Inventory (STAI), and Deliberate Self-Harm Inventory (DSHI) was administered to participants. Data were analysed by means of descriptive statistics, Chi Square tests, t-tests, and logistic regression analyses. The findings suggest high rates of DSH among the sample (46% lifetime prevalence; 36% 12-month prevalence). No significant gender differences were found in the rates of DSH. Participants from the combined Asian and Coloured racial group reported significantly higher rates of DSH than both White and Black participants. Participants aged 20-21 were significantly more likely to report DSH than those in other age groups. Overall, depression scores in the sample fell within the normal range (M = 15.79), while anxiety scores were found to be exceptionally high (state anxiety: M = 46.56; trait anxiety: M = 48.72). The findings suggest that participants with elevated levels of depression are significantly more likely to report DSH. A significant, negative relationship was found between DSH and state anxiety, while a positive yet insignificant relationship was found between DSH and trait anxiety. The findings of this exploratory study partially support the findings of international research investigating the relationship between DSH and depression and anxiety, but warrant further exploration in order to better understand the complexities of these relationships, particularly in the South African context. / Mini-Dissertation (MA)--University of Pretoria, 2014. / tm2015 / Psychology / MA / Unrestricted

Bivariate and multivariate statistical analyses were used to predict the spatial distribution of landslides in the Cuyahoga River watershed, northeastern Ohio, U.S.A. The relationship between landslides and various instability factors contributing to their occurrence was evaluated using a Geographic Information System (GIS) based investigation. A landslide inventory map was prepared using landslide locations identified from aerial photographs, field checks, and existing literature. Instability factors such as slope angle, soil type, soil erodibility, soil liquidity index, landcover pattern, precipitation, and proximity to stream, responsible for the occurrence of landslides, were imported as raster data layers in ArcGIS, and ranked using a numerical scale corresponding to the physical conditions of the region. In order to investigate the role of each instability factor in controlling the spatial distribution of landslides, both bivariate and multivariate models were used to analyze the digital dataset. The logistic regression approach was used in the multivariate model analysis. Both models helped produce landslide susceptibility maps and the suitability of each model was evaluated by the area under the curve method, and by comparing the maps with the known landslide locations. The multivariate logistic regression model was found to be the better model in predicting landslide susceptibility of this area. The logistic regression model produced a landslide susceptibility map at a scale of 1:24,000 that classified susceptibility into four categories: low, moderate, high, and very high. The results also indicated that slope angle, proximity to stream, soil erodibility, and soil type were statistically significant in controlling the slope movement.

In this thesis the relation between gender diversity and innovation in technology and manufacturing companies is explored. Data on firm-level are used from The Enterprise Surveys of the World Bank, which are designed as a panel data survey and comprise a collection of data on 146,000 firms in 143 countries, from years 2006 to 2019. Our focus group is technology and manufacturing firms, therefore, the final data used comprises of 8,839 firms in 47 countries for the year 2013 to investigate whether gender diversity is positively related to innovation of technology and manufacturing firms. Binary logistic regression analysis is used due to the nature of the available data measuring innovation output, which is the survey answer whether a firm introduced a new product/process or not. There are controversies in current research findings caused by the classification of incremental and radical innovation, therefore, this thesis takes an inclusive approach that accounts for total innovation (both incremental and radical). We also assess the total innovation in terms of new products and processes. Gender diversity is measured as total gender diversity of the permanent full-time employees and also on top management level. We also control for industry type, firm size, firm structure, firm’s export activity, R&D investment and employee training. The results show that there are currently low levels of gender inclusion on various firm levels globally. The regression analysis shows that only female presence on top management level made a unique statistically significant contribution to the model, and not total gender diversity on employee level. Regarding the control variables, only firm size, having invested in R&D, and offering employee training made a unique statistically significant contribution to the model. Conclusively, we found that gender diversity on top management level is positively related to innovation performance of technology and manufacturing firms, but not on employee level. However, due to the nature of panel data surveys when it is not possible to lag the cause with respect to the effect, a ‟cause-effect” relationship cannot be deduced with confidence. Nevertheless, our results are in line with the existing theory which indicates that gender diversity on leadership level may have a small but positive effect on achieving firm goals and innovative ideas-decisions-strategies. An explanation why we did not find a positive relationship on employee level can be the fact that during the innovation process the role of individuals and thus gender is invisible - hidden within processes, organizations, systems and there is lack of separating creativity from implementation; employee diversity might improve the creative process but impede the implementation. It is probably easier to assess the role of individuals on top management level and compare the effect of different leadership styles across companies. For external observers this assessment seems more complicated on employee level, thus the benefits of gender diversity even on employee level should not be underestimated. Therefore, more gender diversified quota and policies may need to be taken by decision makers with potentially positive impact both on society and economy. The relationship between innovation and gender diversity is a rather complex subject, affected by many internal and external firm contexts. By accounting for control variables including firm size, structure, export activity, R&D investment, employee training and industry type, some possible causal factors are eliminated. As prior research has already indicated, other factors that have not been addressed yet (and not covered by our framework either) are firm level structures, capabilities, innovation strategies, management style, team structure-functional diversity.

京都大学 / 0048 / 新制・課程博士 / 博士(社会健康医学) / 甲第19638号 / 社医博第71号 / 新制||社医||9(附属図書館) / 32674 / 京都大学大学院医学研究科社会健康医学系専攻 / (主査)教授 中山 健夫, 教授 今中 雄一, 教授 松原 和夫 / 学位規則第4条第1項該当 / Doctor of Public Health / Kyoto University / DFAM

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The spread of new technologies takes place at different speeds across different places and between different individuals in a society. This essay focuses on examining and analyzing how the distribution pattern and pace of electric and hybrid cars, commonly called electric vehicles, has taken place in Sweden since the beginning of the 2000s and the following two decades. This trend analysis has been done at national, regional, and local level. Furthermore, a binary logistical regression analysis has been conducted at the individual level for the corresponding geography, based on the goal of finding the personal conditions that affect the acquisition of an electric vehicle.  The study shows that Sweden is at a turning point regarding the diffusion of innovation where the next phase that follows is the “early acceptance”- face in a society. Regional primary locations lead the development and the individuals who most likely own electric vehicles are generally married, highly educated men who own their own housing, have a yearly income above the national median income level and are between 40-80 years old.

The objective of this research study is to explore trajectories of health-related quality of life (HRQoL) in older breast cancer survivors, along with their predictors. HRQoL is important because patients who show severe symptoms may wish to consider therapies or treatment plans that lead to better HRQoL. Older people are more vulnerable to low HRQoL scores since old age is associated with deteriorating health, multiple comorbidities, and low-socioeconomic status. To examine the HRQoL trajectory among older women with breast cancer, we used the data queried from the Surveillance, Epidemiology and End Results Medicare Health Outcomes Survey database. A total of 1,089 older (≥ 65 years) women who were diagnosed with breast cancer in 1998-2012 and participated in the survey before and after the cancer diagnosis were identified. HRQoL was measured using SF-36/VR-12 questionnaire and summarized as Physical Component Summary (PCS) Score and Mental Component Summary (MCS) Score. Latent Class Growth Mixture Modeling was conducted to identify distinct groups of women with a similar trajectory of HRQoL. The results showed that there were three latent classes of HRQoL trajectories for PCS: the high-declining (46.5% of the sample), mid-declining (36.0%), and the low-improving (17.5%). Two latent classes of HRQoL trajectories were identified for MCS: high-stable (76.5%) and low-declining (23.5%). The results showed that age at diagnosis, BMI, level of education, geographic region, tumor grade, tumor size, and number of comorbidities were some of the major predictors of health-related quality of life. These predictors were further explored using multinomial logistic regression analysis which identified number of comorbidities as the most significant predictor for HRQoL-PCS scores and level of education as the most significant predictor for HRQoL-MCS scores. This suggests that future research needs to be conducted, identifying the most common comorbidities in older breast cancer survivors to develop interventions that better the physical HRQoL in patients, in addition to the development of mental HRQoL interventions for patients that are less educated.