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Does Federalism Prevent Democratic Accountability? Assigning Responsibility for Rates of COVID-19 Testing

ABSTRACT: Does federalism prevent citizens from holding governments accountable for their actions? The pandemic represents the ideal scenario for testing the effects of federalism on democratic accountability because citizens are highly motivated to hold governments accountable for preventing or failing to prevent the rapid transmission of the virus. Previous research suggests that a number of institutional and political factors complicate the accountability function in federal systems. We add to this literature by assessing the effect of one political factor, exclusivity (measured in terms of policy variation at one level), on accountability. The coronavirus pandemic provides a unique opportunity to assess this factor given the high levels of issue saliency, media attention, and low levels of intergovernmental and interparty conflict it has generated. Drawing on original data from the May 2020 Democratic Checkup Survey and public data from the Canadian National Microbiology Laboratory, our preliminary findings suggest that interprovincial policy variation with respect to coronavirus testing is not correlated with public assessments of the adequacy of provincial testing, and so it seems that Canadians are not able to assign responsibility to the correct level of government despite ideal conditions for doing so.

SUBMITTER: Kennedy J

PROVIDER: S-EPMC8805446 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThis paper aims to provide insights into the role of traditional authorities in two maternal health programmes in Northern Malawi. Among strategies to improve maternal health, these authorities issue by-laws: local rules to increase the uptake of antenatal and delivery care. The study uses a framework of gendered institutions to critically assess the by-law content, process and effects and to understand how responsibilities and accountabilities are constructed, negotiated and reversed.MethodsFindings are based on a qualitative study in five health centre catchment areas in Northern Malawi. Data were collected using meeting observations and document search, 36 semi-structured individual interviews and 19 focus group discussions with female maternal health service users, male community members, health workers, traditional leaders, local officials and health committee members. A gender and power sensitive thematic analysis was performed focusing on the formulation, interpretation and implementation process of the by-laws as well as its effects on women and men.ResultsIn the study district, traditional leaders introduced three by-laws that oblige pregnant women to attend antenatal care; bring their husbands along and; and to give birth in a health centre. If women fail to comply with these rules, they risk being fined or denied access to maternal health services. The findings show that responsibilities and accountabilities are negotiated and that by-laws are not uniformly applied. Whereas local officials support the by-laws, lower level health cadres' and some community members contest them, in particular, the principles of individual responsibility and universality.ConclusionsThe study adds new evidence on the understudied phenomenon of by-laws. From a gender perspective, the by-laws are problematic as they individualise the responsibility for maternal health care and discriminate against women in the definition and application of sanctions. Through the by-laws, supported by national policies and international institutions, women bear the full responsibility for failures in maternal health care, suggesting a form of 'reversed accountability' of women towards global maternal health goals. This can negatively impact on women's reproductive health rights and obstruct ambitions to achieve gender inequality and health equity. Contextualised gender and power analysis in health policymaking and programming as well as in accountability reforms could help to identify these challenges and potential unintended effects.

Project description:Background and objectiveThe novel coronavirus disease 2019 (COVID-19) is considered a pandemic by the World Health Organization (WHO). As of April 3, 2020, there were 1,009,625 reported confirmed cases, and 51,737 reported deaths. Doctors have been faced with a myriad of patients who present with many different symptoms. This raises two important questions. What are the common symptoms, and what are their relative importance?MethodsA non-structured and incomplete COVID-19 dataset of 14,251 confirmed cases was preprocessed. This produced a complete and organized COVID-19 dataset of 738 confirmed cases. Six different feature selection algorithms were then applied to this new dataset. Five of these algorithms have been proposed earlier in the literature. The sixth is a novel algorithm being proposed by the authors, called Variance Based Feature Weighting (VBFW), which not only ranks the symptoms (based on their importance) but also assigns a quantitative importance measure to each symptom.ResultsFor our COVID-19 dataset, the five different feature selection algorithms provided different rankings for the most important top-five symptoms. They even selected different symptoms for inclusion within the top five. This is because each of the five algorithms ranks the symptoms based on different data characteristics. Each of these algorithms has advantages and disadvantages. However, when all these five rankings were aggregated (using two different aggregating methods) they produced two identical rankings of the five most important COVID-19 symptoms. Starting from the most important to least important, they were: Fever/Cough, Fatigue, Sore Throat, and Shortness of Breath. (Fever and cough were ranked equally in both aggregations.) Meanwhile, the sixth novel Variance Based Feature Weighting algorithm, chose the same top five symptoms, but ranked fever much higher than cough, based on its quantitative importance measures for each of those symptoms (Fever - 75 %, Cough - 39.8 %, Fatigue - 16.5 %, Sore Throat - 10.8 %, and Shortness of Breath - 6.6 %). Moreover, the proposed VBFW method achieved an accuracy of 92.1 % when used to build a one-class SVM model, and an NDCG@5 of 100 %.ConclusionsBased on the dataset, and the feature selection algorithms employed here, symptoms of Fever, Cough, Fatigue, Sore Throat and Shortness of Breath are important symptoms of COVID-19. The VBFW algorithm also indicates that Fever and Cough symptoms were especially indicative of COVID-19, for the confirmed cases that are documented in our database.

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Does Federalism Prevent Democratic Accountability? Assigning Responsibility for Rates of COVID-19 Testing

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