Infrastructural artefacts in community health: a case study of pregnancy care infrastructures in south India

The work of frontline health workers providing access to pregnancy care services to women in South India is highly distributed and often overlooked in the design of healthcare infrastructures. Unlike clinical and nonclinical personnel who engage in different care practices within and across hospital departments with clearly established work roles, the work of frontline workers is performed across different geographical areas beyond the boundaries of the hospital and with loosely defined roles and resources making the coordination of work more complex. Based on a case study investigating the work of frontline health workers, we report a number of material infrastructural arrangements (the Thayi Card, physical and digital registers, and mobile phones) that played a major role supporting community health practices. We conclude by discussing the opportunities that these artefacts offer for the design of healthcare infrastructures

Embodied negotiations, practices and experiences interacting with pregnancy care infrastructures in South India

Behavior change and improving health literacy based on normative ideals of motherhood is a dominant paradigm to address maternal health challenges. However, these ideals often remove women's control over their bodies overlooking how the bodily experiences of pregnancy are socially and culturally constructed. We report on 27 interviews with pregnant women and nursing mothers in rural and semi-urban areas of South India, and six focus groups with 23 frontline health workers as secondary data. We explore how the embodied pregnancy experiences are influenced and negotiated by the socio-cultural context and existing care infrastructures. Our findings highlight how the ways of seeing, knowing, and caring for a body of a pregnant woman through often conflicting norms, beliefs and practices of medicine, nourishment and care actively shape the experiences of pregnancy. We open up a space for novel opportunities for digital health technologies to enhance women's embodied experiences and pregnancy care infrastructures in the Global South

Virality and the Virus: Drugs and Cures on Twitter During the COVID-19 Crisis in India

Social media platforms often become environments of information ambiguity amidst crisis events. We studied the discussion around four "cures" for COVID-19 in India, site of the highest number of recorded positive cases, between 2020 and May 2021, focusing on the role played by high network accounts on social media such as those of journalists, politicians, and celebrities. We find that information paucity led to social media influencers wielding a more important voice online than institutional sources and experts, leading to massive spikes in demand for unproven cures during the peak of the crisis

Electricity Price and Load Forecasting using Enhanced Convolutional Neural Network and Enhanced Support Vector Regression in Smart Grids

Short-Term Electricity Load Forecasting (STELF) through Data Analytics (DA) is an emerging and active research area. Forecasting about electricity load and price provides future trends and patterns of consumption. There is a loss in generation and use of electricity. So, multiple strategies are used to solve the aforementioned problems. Day-ahead electricity price and load forecasting are beneficial for both suppliers and consumers. In this paper, Deep Learning (DL) and data mining techniques are used for electricity load and price forecasting. XG-Boost (XGB), Decision Tree (DT), Recursive Feature Elimination (RFE) and Random Forest (RF) are used for feature selection and feature extraction. Enhanced Convolutional Neural Network (ECNN) and Enhanced Support Vector Regression (ESVR) are used as classifiers. Grid Search (GS) is used for tuning of the parameters of classifiers to increase their performance. The risk of over-fitting is mitigated by adding multiple layers in ECNN. Finally, the proposed models are compared with different benchmark schemes for stability analysis. The performance metrics MSE, RMSE, MAE, and MAPE are used to evaluate the performance of the proposed models. The experimental results show that the proposed models outperformed other benchmark schemes. ECNN performed well with threshold 0.08 for load forecasting. While ESVR performed better with threshold value 0.15 for price forecasting. ECNN achieved almost 2% better accuracy than CNN. Furthermore, ESVR achieved almost 1% better accuracy than the existing scheme (SVR)

The Invisible Work of Maintenance in Community Health: Challenges and Opportunities for Digital Health to Support Frontline Health Workers in Karnataka, South India

Frontline health workers are the first and often the only access point to basic health care services in low-andmiddleincome countries. However, the work and the issues frontline health workers face are often invisible tothe healthcare system, with limited resources to assist them. This study explores the work practices, challengesand roles of frontline health workers in community health with particular focus on pregnancy care in SouthIndia. Drawing on the notion of maintenance and articulation work, we describe the maintenance work offrontline health workers maintaining, anticipating, reconciling, and supporting care infrastructures beyonddata collection practices. Our findings highlight how socio-cultural practices, perceptions, status, and existingsystems influence maintenance work practices. Based on our findings, we suggest moving beyond the focus ontraining and performance to design CSCW tools to support the maintenance work of frontline health workersas ‘system-builders’ to make healthcare infrastructures work in community health.</div

DISMISS: Database of Indian Social Media Influencers on Twitter

Databases of highly networked individuals have been indispensable in studying narratives and influence on social media. To support studies on Twitter in India, we present a systematically categorized database of accounts of influence on Twitter in India, identified and annotated through an iterative process of friends, networks, and self-described profile information, verified manually. We built an initial set of accounts based on the friend network of a seed set of accounts based on real-world renown in various fields, and then snowballed ``friends of friends" multiple times, and rank ordered individuals based on the number of in-group connections, and overall followers. We then manually classified identified accounts under the categories of entertainment, sports, business, government, institutions, journalism, civil society accounts that have independent standing outside of social media, as well as a category of ``digital first" referring to accounts that derive their primary influence from online activity. Overall, we annotated 11580 unique accounts across all categories. The database is useful studying various questions related to the role of influencers in polarization, misinformation, extreme speech, political discourse etc