Domestic Violence Survivorship Among a Sample of Older African American Women: An Interpretative Phenomenological Analysis

African American women experience domestic violence at a rate that is 35% higher than the domestic violence rate of White women. Moreover, African American women encompass 8% of the US population but account for 22% of domestic violence–related homicide victims. African American women are at greater risk for facing economic difficulties, homelessness, unemployment, and health and educational disparities due to domestic violence. However, little attention has been given to the lived experiences of older women who are domestic violence survivors. Theories of phenomenology and hermeneutics provided the theoretical framework for this study. An interpretative phenomenological analysis framework guided the research design and data analysis process. Overall, the findings pointed toward the need for generating more culturally sensitive and culturally tailored programs and services to address domestic violence in the African American community. Additionally, services are needed that include faith-based education, training, and prevention programs to enable churches and social workers to address the unique cultural needs of African American women who have endured domestic violence.

Methodological Perspectives on the Study of the Health Effects of Unemployment – Reviewing the Mode of Unemployment, the Statistical Analysis Method and the Role of Confounding Factors

Introduction: Studying the relationship between unemployment and health raises many methodological challenges. In the current study, the aim was to evaluate how different ways of measuring unemployment and the choice of statistical model affects the effect estimate. Methods: The Northern Swedish cohort was used, and two follow-up surveys thereof from 1995 and 2007, as well as register data about unemployment. Self-reported current unemployment, self-reported accumulated unemployment and register-based accumulated unemployment were used to measure unemployment and its effect on self-reported health was evaluated. Analyses were conducted with G-computation, logistic regression and three estimators for the inverse probability weighting propensity scores, and 11 potentially confounding variables were part of the analyses. Results were presented with absolute differences in the proportion with poor self-reported health between unemployed and employed individuals for all estimators but logistic regression. Results: Of the initial 1083 pupils in the cohort, 488–693 individuals were defined as employed and 61–214 individuals were defined as unemployed in our different analyses. In the analyses, the deviation was large between the unemployment measures, with a difference of at least 2.5% in effect size when unemployed was compared with employed for the self-reported and register-based unemployment modes. The choice of statistical method only had a small influence on effect estimates and the deviation was in most cases lower than 1%. When models were compared based on the choice of potential confounders in the analytical model, the deviations were rarely above 0.6% when comparing models with 4 and 11 potential confounders. Our variable for health selection was the only one that strongly affected estimates when it was not part of the statistical model. Conclusions: Misspecifications of the statistical model or choice of analytical method might not matter much for effect estimates of the relationship between unemployment and health except for the inclusion of a variable measuring earlier health status before becoming unemployed. On the other hand, how unemployment is measured is highly important.

Crises and social policy preferences: The impact of Covid-19 in Britain

Do crises substantially change public support for taxes and spending, and why? We leverage the multifaceted character of the Covid-19 pandemic to test different theoretical micro-mechanisms usually confounded in observational research, or tested in isolation. Our randomized survey experiment provides four main findings. First, the economic and health dimensions of the crisis generated a substantial left-wing turn among the British public. Second, the effects are stronger on spending priorities (unemployment and health policies) than on who should pay for the welfare bill (progressivity of income and wealth taxes). Third, economic self-interested motivations are not relevant mechanisms to explain our findings. Fourth, framings associated with open borders and the global spread of the virus polarized welfare attitudes along immigration policy preferences. The generalizability of our findings, the prospects of redistributive conflicts after Covid, and the validity of established theories of welfare preferences in times of crisis are discussed.

SOCIO-ECONOMIC STRUCTURE AND DEVELOPMENT: LEVELS OF DEVELOPMENT IN RUDRAPRAYAG DISTRICT, UTTARAKHAND, INDIA

The development planning in India has been started from First five year plan in 1951. There was focused on the agriculture. Later on, Industries, education, poverty, unemployment and health etc. all the sectors have been touched. Today, there is going on 12th five year plan. But, it is an illusively higher truth that even with the introduction of a number and varied development programs in India, the country in general and backward areas in particular, have not tasted the fruits of development as yet due to serious reasons (Purohit, 1986). The mountains are not good at the flat land conditions which do not attract more to the agricultural and industrial developments. There are 70.64% of marginal land holder out of total holdings and 17.77% of small land holder out of total holdings (Mittal, 2008). So we need to utilize the agricultural field very carefully. Thus, rural out-migration is dominant in Uttarakhand due to the negative relationship between population and regional development (Bhandari, 2007).Therefore, the figure of socio-economic structure is carried out and the study area is characterized by different developmental zones in this paper.

Towards Atmanirbhar Bharat Abhiyan through Skill Development and Entrepreneurship: A Review

Nations across the globe are struggling with the sudden occurrence of the novel coronavirus. In the more challenging times of havoc caused by the pandemic COVID-19, the Indian government has taken several steps to ensure the country's growth and has raised India's willpower to survive economically and socially. There are many alarming situations like mobility, migration of workers, unemployment, and health-hazardous that have arisen primarily due to the pandemic in the country, which led to the birth of the conceptualization of Atmanirbhar Bharat Abhiyan. The narrative of Atmanirbhar Bharat has pitched the rhythm of five key pillars, namely: Economy, infrastructure, system, demography, and demand. In this pursuit, this study analyzed the relationship between India's skillsets for entrepreneurship encompassing the self-dependent concept of Atmanirbhar Bharat. The article has tried emphasizing each pillar of Atmanirbhar Bharat, fostering skill development and entrepreneurship in the nation. The study assessed that the economy as the prime pillar of a nation required significant and well-versed investment in R & D. The nation required investment in the development of individual skills, dependence on regional resources, capital, and laborers. In terms of Infrastructure as the second pillar, it was assessed that strengthening of skill and entrepreneurial hubs serves as a valuable investment in infrastructure to support skill development and entrepreneurship. However, the cooperative efforts of key national actors jointly will set convenient conditions for mainstreaming and diffusion of the technology-driven system as the third pillar. The study assessed that India having vibrant demography with a young population serves as the crucial factor that can anticipate demand with supply forecasting. It will ultimately stimulate and lead the nation toward India's initiative self-reliant.

Theft, Opioid Pills, Unemployment, and Insurance: A Longitudinal Analysis of American Counties in the Wake of the Opioids Crisis

This study examines the relationships between prescription opioid pills, unemployment, health insurance, and theft. Covering the years 2006–2012, our data are an aggregate of information from the Drug Enforcement Administration, the Federal Bureau of Investigation’s National Incident-Based Reporting System, and the American Community Survey (ACS). The unit of analysis is time nested within counties. Preliminary results demonstrate that there were approximately 46 prescription opioid pills distributed per person annually in the United States between 2006 and 2012. Multivariate results reveal that counties with higher numbers of prescription opioid pills tend to experience significantly higher patterns of theft. Interestingly, health insurance is positively associated with theft while unemployment appears to protect against theft. The relationship between pills and theft is also conditioned by both unemployment and health insurance. Future research should explore these relationships to better inform efforts at making responsible social policy in the midst of the opioids crisis.

Is remote working here to stay? Lessons and ideas for a post-pandemic future

The article studies the impact of the Covid-19 pandemic on the labor market for what concerns the diffusion of remote working in Italy. First, it shows how working remotely represents a possibility for a minority of the workforce. Then, it discusses the presence of structural socioeconomic gaps between those who can and cannot work remotely in terms of income, unemployment, and health security at work. Finally, it addresses the issue of poor regulation on remote working by offering an overview of the national regulatory framework and describing recent trends in collective bargaining.