Psychological Impact of COVID-19 Pandemic on Frontline Responders and Students in Training

Medical personnel have been in the frontlines of the pandemic leading to increased levels of stress and an impact on mental health. Risks may include, but are not limited to, pronounced burnout (Shechter et al., 2020), vicarious trauma, and post-traumatic stress disorder. The goal of this investigation was to gain insight on the psychological effects that the pandemic had on both frontline responders (EMTs and emergency room staff) and students in clinical training. Emerging adults and adult participants (N=150; ages 18-46; 70.4% ages 18-24) were recruited through the introductory psychology subject pool, community healthcare, and social media. Linear regression and means testing were employed to assess differences between current frontline workers and future workers on the Depression, Anxiety, and Stress Scale (DASS-21; Lovibond, 1995) on irritability, sleep, covid-19 positive presence, concentration, and other mental health factors. Hierarchical linear regression, controlling for age, indicated higher anxiety subscale scores (b=2.49, p=.008) and higher stress subscale scores (b=2.25, p=.035) were present on the DASS-21 for women. Dichotomous means testing indicated higher anxiety, stress, and depression levels for those who also reported a significant change in sleep habits (p <.001) and for those who reported being more irritable on their days off (p <.001) during the pandemic. Students in training (37.7%) indicated interest in considering a different career path (r = .302, p = .02). Future studies should examine these dynamic relationships among mental health factors among healthcare professionals and the implications for training the next generation.

The effect of social benefit reform on educational inequality

Cross-country research argues that the design of welfare states and social protection systems shapes the intergenerational transmission of inequality. Studies that examine this relationship within a country are however lacking from the literature. Using difference-in-differences estimation and data from the Socio-Economic Panel, I analyse whether children of unemployment assistance recipients have lower educational attainment after changes to eligibility criteria, benefit levels and conditionality were introduced in Germany in 2005. I find that differences in the probability to attend the academic secondary school track between children of unemployment assistance recipients and children living in families, where no benefits are claimed, increased by 13 percentage points. In part, this was driven by the introduction of means-testing that changed the composition of unemployment assistance recipients towards the more disadvantaged. However, a further worsening in the financial conditions of these already disadvantaged families following reductions in benefit criteria appear as the main driver of the observed effect. By contrast, changes in parental subjective wellbeing due to increased benefit conditionality and stigma do not appear to play a significant mediating role.

Politics of Universal and Unconditional Cash Transfer: Examining Attitudes Toward Universal Basic Income

Universal Basic Income (UBI) is a periodic cash payment to all residents in a jurisdiction, without obligation. Universalism and unconditionality distinguish UBI from other redistributive policies that require means testing and certain behaviors to gain and maintain eligibility. Despite an increased interest in UBI, it is poorly understood how these two critical features – universalism and unconditionality – influence public attitudes toward UBI. This paper explores results of the eighth round of the European Social Survey and finds that people who support unconditionality are more likely to support UBI, as expected. But support for UBI is also significantly associated with a desire to help the poor rather than provide universal cash transfers to all individuals.

Ireland

This chapter provides an extended look at health politics and the largely tax-financed health system in Ireland. It traces the historical development of the Irish healthcare system, characterized by the institutionalization of a health service that obliged and incentivized the middle classes to pay for their healthcare, out-of-pocket or through voluntary private health insurance. Since the late 1980s, the hospital sector has become more privatized, while universal coverage has been partially introduced to the primary sector. While center-right government legislation which institutionalized the treatment of private patients in public hospitals elicited strong parliamentary opposition from across the political spectrum, the fiscal incentivization of private hospital development, introduced by a center-right coalition, was subject to little debate. The most significant turning point in healthcare policy since 1989 has been the removal of means-testing and provision of free general practitioner care to the under-6s and the over-70s. Cross-party consensus on a plan to move towards a universal tax-based healthcare system was reached in 2017.

9. Funding of Criminal Defence Services and First Appearance Process

This chapter considers the public funding of criminal proceedings and the early stages of the criminal litigation process. Topics discussed include legal aid as a human right; pre-charge advice and assistance; funded representation in court; representation orders; the interests of justice test; means testing and its application to cases tried in the magistrates’ court; the means test as applied to cases triable on indictment; work that can be done under a representation order; acquitted defendants and Defendants’ Costs Order; the future of public funding; and preparing for the first appearance before the magistrates’ court.

Challenges in the implementation of the City of Johannesburg’s Expanded Social Package in alleviating COVID-19 induced food insecurity

Background: The coronavirus disease 2019 (COVID-19) pandemic has subjected the African urban population into abject poverty. Local government initiatives, such as the City of Johannesburg’s (CoJ) Expanded Social Package (ESP) ‘Siyasizana’ [we help each other], have been established to enhance food security amongst the city’s most vulnerable based on their level of income.Aim: This article analysed the extent to which the ESP was effective in addressing food insecurity in the wake of COVID-19.Setting: This research was descriptive and explanatory in nature that played an important role in obtaining an in-depth interpretation of the challenges of the implementation of ESP in mitigating food insecurity in the CoJ.Methods: This article utilised a qualitative case study design with the aid of existing literature, municipal documents and authoritative internet sources in order to analyse the extent to which the ESP is effective in addressing food insecurity in the wake of COVID-19.Results: This article found out that the ESP did not expressly address the COVID-19-induced food insecurity because of numerous challenges, namely awareness, stigmatisation, qualification and hidden costs.Conclusion: The CoJ should bring in private players to finance the programme as COVID-19 has increased the number of beneficiaries, relax the requirement for in-person application to allow for online registration, increase the ESP poverty threshold of R6100 to cater for those on the border of poverty and diversify its means testing to include other criteria such as Unemployment Insurance Fund (UIF), which will provide much needed relief to those who might have lost income.

Student Loans Financing in Tanza:

This study examined the mechanisms employed to finance student loans in Tanzania and who benefits and how. The findings show that student loans are financed by the public exchequer. The number of students fromhigh-income families accessing these loans is disproportionate to their representation in Higher Education Institutions, while the share for middle and low-income students reflects their representation. There is also animbalance between male and female beneficiaries across programmes, notably in the Science, Technology, Engineering, and Math (STEM) disciplines. It is concluded that such disparities are the result of the fact thatthe student loan scheme seeks to satisfy a number of government policy objectives in relation to higher education beyond access and equity, and that means testing is not rigorously conducted. Key words: Higher Education, higher education policy, financing higher education, higher education student loans, public subsidisation of higher education

Poverty Relief Programs in Postcommunist Countries: A Case Study on the Albanian and Chinese Programs

Social assistance is a cash transfer program targeting the poorest households. China has created the Dibao (DB), meaning minimum livelihood guarantee, the most extensive unconditional cash transfer program globally with over 70 million people covered, whereas in Albania, the Ndhime Ekonomike (NE) meaning financial help covers around 15% of the total working-age population. Both programs are means-tested, have strict requirements for eligibility, and have been enlarged and modified in time to improve targeting and tackling leakage. In this article, we will look at similarities and common issues first, and then calculate the cost of enlarging both programs to all working-age population with no means-testing. We argue that a UBI (universal basic income) can increase private expenditure in health and education while costing less than 1% of gross domestic product (GDP) in both countries’ rural areas. We will conclude by looking at how the COVID-19 outbreak is pushing developing countries toward a UBI by first adopting a temporary basic income (TBI).

Using Predictive Analytics for Public Policy: The Case for Lost Work due to the COVID-19

In this brief research article, I demonstrate how predictive analytics or machine learning can be used to predict outcomes that are of interest in public policy. I developed a predictive model that determined who were not able to work during the past four weeks because the COVID-19 pandemic led their employer to close or lose business. I used the Current Population Survey (CPS) collected from May to November 2020 (N=352,278). Predictive models considered were logistic regression and ensemble-based methods (bagging of regression trees, random forests, and boosted regression trees). Predictors included (1) individual-, (2) family-, (3) and community or societal- level factors. To validate the models, I used the random training test splits with equal allocation of samples for the training and testing data. The random forest with the full set of predictors and number of splits set to the square root of the number of predictors yielded the lowest testing error rate. Predictive analytics that seek to forecast the inability to work due to the pandemic can be used for automated means-testing to determine who gets aid like unemployment benefits or food stamps.