Abstract (en): The Global Digital Activism Data Set (GDADS), released February 2013 by the Digital Activism Research Project (DARP) at the University of Washington in Seattle, features coded cases of online digital activism from 151 countries and dependent territories. Several features from each case of digital activism were documented, including the year that online action commenced, the country of origin of the initiator(s), the geographic scope of their campaign, and whether the action was online only, or also featured offline activities. Researchers were interested in the number and types of software applications that were used by digital activists. Specifically, information was collected on whether software applications were used to circumvent censorship or evade government surveillance, to transfer money or resources, to aid in co-creation by a collaborative group, or for purposes of networking, mobilization, information sharing, or technical violence (destructive/disruptive hacking). The collection illustrates the overall focus of each case of digital activism by defining the cause advanced or defended by the action, the initiator's diagnosis of the problem and its perceived origin, the identification of the targeted audience that the campaign sought to mobilize, as well as the target whose actions the initiators aimed to influence. Finally, each case of digital activism was evaluated in terms of its success or failure in achieving the initiator's objectives, and whether any other positive outcomes were apparent. Through GDADS and associated works, DARP aims to study the effect of digital technology on civic engagement, nonviolent protest, and political change around the world. The GDADS contains three sets of data: (1) Coded Cases, (2) Case Sources, and (3) Coded Cases 2.0. The Coded Cases dataset contains 1179 coded cases of digital activism from 1982 through 2012. The Case Sources dataset is an original deposited Excel document that contains source listings from all cases documented by researchers, including those that were ultimately excluded from the original Coded Cases dataset. Coded Cases 2.0 contains 426 additional cases from 2010 through 2012; these cases were treated with a revised coding scheme and an extended review process. GDADS was assembled with the following inclusion criteria: cases needed to exhibit either (1) an activism campaign with at least one digital tactic, or (2) an instance of online discourse aimed at achieving social or political change, and (3) needed to be described by a reliable third party source. In addition to these inclusion criteria, researchers required that the digital activism be initiated by a traditional civil society organization, such as a nongovernmental organization or a nonprofit, or by the collaborative effort of one or more citizens. Digital activism cases initiated by governments or for-profit entities were not included in the collection. The data were assembled by a team of volunteers searching Web sites that are known to document global digital activism; researchers also collected data from peer reviewed journal articles that included digital activism case studies. This data collection does not feature a weighting scheme. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Checked for undocumented or out-of-range codes.. Global occurrences of online digital activism and journal article case studies of digital activism from 1982 through 2012. Smallest Geographic Unit: country Dataset 1: Coded Cases, contains the entire collection of coded cases, according to the inclusion criteria, for 1982-2009, but is incomplete for 2010-2012. Dataset 2: Case Sources, is an original deposited Excel document that contains links and citations used to code dataset 1 cases, plus 166 cases collected but not included in dataset 1. Dataset 3: Coded Cases 2.0, contains additional cases using purposive, multi-source, multilingual, sampling. For more information on sampling, please refer to the Methodology section in the ICPSR Codebooks. 2014-06-12 The collection has been updated with file set 3, Coded Cases 2.0, which contains additional cases that use an updat...

Precision, recall and f1-score of a Random Forest with 9 trees of a maximum depth equals 10 after SMOTE is applied on the training set.

The coronavirus disease (COVID-19) spread rampantly around the world at the beginning of 2020 before the governments of each country could prevent it by making decisions based on medical data analysis. With proper formalization, the terabytes of new textual data available online every day could have been used for the early description and detection of cases of this virus. Since then, the number of Event-Based Surveillance (EBS) applications has increased exponentially. These applications aim to mine channels of unstructured information to detect signs of possible public health events' progression. However, one problem with such systems is the need for expert intervention to define which event will be captured, which relevant terms should be used in the search, and to analyze the events to modify the search procedure constantly. Another problem is that many of these applications do not consider both spatial and temporal characteristics. Addressing such limitations, this datasets presents a novel approach. We propose the use of BioPropaPhenKG to replace such systems. In this dataset, BioPropaPhen was enhanced with information comming from unstructured texts from online newspapers and medical articles. BioPropaPhenKG, its ontology and other useful information can be found in https://zenodo.org/records/10911980. The code used for this use case can be found in https://github.com/Gabriel382/DDPF-Health-Risks . Finally, the datasets used where UMLS MetamorphoSys, OpenStreetMaps, Wikidata, Aylien (data only from November of 2019) and CORD-19 (data only from December of 2019).

To read, you just need to load it with Neo4j:4.4.3. Alternatively, you can open it with docker using the following command:

docker run --interactive --tty --rm \
--publish=7474:7474 --publish=7687:7687 \
--volume=/path-to-data-folder:/data --user="$(id -u):$(id -g)"\
neo4j:4.4.3 \
neo4j-admin load --from=/data/BioPropaPhenKG-Journal-Medical.dump --database "neo4j" --force

The Global Wind Power Tracker (GWPT) is a worldwide dataset of utility-scale wind facilities. It includes wind farm phases with capacities of 10 megawatts (MW) or more. A wind project phase is generally defined as a group of one or more wind turbines that are installed under one permit, one power purchase agreement, and typically come online at the same time. The GWPT catalogs every wind farm phase at this capacity threshold of any status, including operating, announced, under development, under construction, shelved, cancelled, mothballed, or retired. Each wind farm included in the tracker is linked to a wiki page on the GEM wiki.

Architecture

Global Energy Monitor’s Global Wind Power Tracker uses a two-level system for organizing information, consisting of both a database and wiki pages with further information. The database tracks individual wind farm phases and includes information such as project owner, status, installation type, and location. A wiki page for each wind farm is created within the Global Energy Monitor wiki. The database and wiki pages are updated annually.

Status Categories

• Announced: Proposed projects that have been described in corporate or government plans but have not yet taken concrete steps such as applying for permits.
• Development: Projects that are actively moving forward in seeking governmental approvals, land rights, or financing.
• Construction: Site preparation and equipment installation are underway.
• Operating: The project has been formally commissioned; commercial operation has begun.
• Shelved: Suspension of operation has been announced, or no progress has been observed for at least two years.
• Cancelled: A cancellation announcement has been made, or no progress has been observed for at least four years.
• Retired: The project has been decommissioned.
• Mothballed: The project is disused, but not dismantled.

Research Process

The Global Wind Power Tracker data set draws on various public data sources, including:

• Government data on individual power wind farms (such as India Central Electricity Authority’s “Plant Wise Details of All India Renewable Energy Projects” and the U.S. EIA 860 Electric Generator Inventory), country energy and resource plans, and government websites tracking wind farm permits and applications;
• Reports by power companies (both state-owned and private);
• News and media reports;
• Local non-governmental organizations tracking wind farms or permits.

Global Energy Monitor researchers perform data validation by comparing our dataset against proprietary and public data such as Platts World Energy Power Plant database and the World Resource Institute’s Global Power Plant Database, as well as various company and government sources.

Wiki Pages

For each wind farm, a wiki page is created on Global Energy Monitor’s wiki. Under standard wiki convention, all information is linked to a publicly-accessible published reference, such as a news article, company or government report, or a regulatory permit. In order to ensure data integrity in the open-access wiki environment, Global Energy Monitor researchers review all edits of project wiki pages.

Mapping

To allow easy public access to the results, Global Energy Monitor worked with GreenInfo Network to develop a map-based and table-based interface using the Leaflet Open-Source JavaScript library. In the case of exact coordinates, locations have been visually determined using Google Maps, Google Earth, Wikimapia, or OpenStreetMap. For proposed projects, exact locations, if available, are from permit applications, or company or government documentation. If the location of a wind farm or proposal is not known, Global Energy Monitor identifies the most accurate location possible based on available information.

The Global Solar Power Tracker is a worldwide dataset of utility-scale solar PV facilities. It includes solar farm phases with capacities of 20 megawatts (MW) or more (10 MW or more in Arabic-speaking countries). A solar project phase is generally defined as a group of one or more solar units that are installed under one permit, one power purchase agreement, and typically come online at the same time. The Global Solar Power Tracker catalogs every solar farm phase at these capacity thresholds of any status, including operating, announced, under development, under construction, shelved, cancelled, mothballed, or retired. Each solar farm included in the tracker is linked to a wiki page on the GEM wiki.

Architecture

Global Energy Monitor’s Global Solar Power Tracker uses a two-level system for organizing information, consisting of both a database and wiki pages with further information. The database tracks individual solar farm phases and includes information such as project owner, status, and location. A wiki page for each solar farm is created within the Global Energy Monitor wiki. The database and wiki pages are updated annually.

Status Categories

• Announced: Proposed projects that have been described in corporate or government plans but have not yet taken concrete steps such as applying for permits.
• Development: Projects that are actively moving forward in seeking governmental approvals, land rights, or financing.
• Construction: Site preparation and equipment installation are underway.
• Operating: The project has been formally commissioned; commercial operation has begun.
• Shelved: Suspension of operation has been announced, or no progress has been observed for at least two years.
• Cancelled: A cancellation announcement has been made, or no progress has been observed for at least four years.
• Retired: The project has been decommissioned.
• Mothballed: The project is disused, but not dismantled.

Research Process

The Global Solar Power Tracker data set draws on various public data sources, including: - Government data on individual power solar farms (such as India Central Electricity Authority’s “Plant Wise Details of All India Renewable Energy Projects” and the U.S. EIA 860 Electric Generator Inventory), country energy and resource plans, and government websites tracking solar farm permits and applications; - Reports by power companies (both state-owned and private); - News and media reports; - Local non-governmental organizations tracking solar farms or permits.

Wiki Pages

For each solar farm, a wiki page is created on Global Energy Monitor’s wiki. Under standard wiki convention, all information is linked to a publicly-accessible published reference, such as a news article, company or government report, or a regulatory permit. In order to ensure data integrity in the open-access wiki environment, Global Energy Monitor researchers review all edits of project wiki pages.

Mapping

To allow easy public access to the results, Global Energy Monitor worked with GreenInfo Network to develop a map-based and table-based interface using the Leaflet Open-Source JavaScript library. In the case of exact coordinates, locations have been visually determined using Google Maps, Google Earth, Wikimapia, or OpenStreetMap. For proposed projects, exact locations, if available, are from permit applications, or company or government documentation. If the location of a solar farm or proposal is not known, Global Energy Monitor identifies the most accurate location possible based on available information.

Survey and interview data from a study on the views and experiences of early career researchers (postdoctoral researchers) around the support from research organisations, funding bodies and career services and how this offer might be improved in the future. This applied to those employed inside and outside of academia. The data result from an online survey of early career social scientists (N=1048), interviews with a subset of the respondents (N=35) and with experts (N=9). The findings informed the strategy for careers advice and support provided by the Economic and Social Research Council through Doctoral Training Partnerships and Centres for Doctoral Training and the creation of new funding strands for early career researchers.

The last two generations have seen a remarkable world-wide transformation of higher education (HE) into a core social sector with continually expanding local and global reach. Most nations are moving towards, or have already become, 'high participation' HE systems in which the majority of people will be educated to tertiary level. In the UK HE is at the same time a pillar of science and the innovation system, a primary driver of productivity at work, a major employer and a mainstay of cities and regions, and a national export industry where 300,000 non-EU students generated over 7 billion in export-related earnings for the UK in 2012-13. In 2012, 60 per cent of UK school leavers were expected to graduate from tertiary education over the lifetime, 45 per cent at bachelor degree level, compared to OECD means of 53/39 per cent. Higher education and the scientific research associated with universities have never been more important to UK society and government. HE is large and inclusive with a key role in mediating the future. Yet it is poorly understood. Practice has moved ahead of social science. There has been no integrated research centre dedicated to this important part of the UK. The Centre for Engaged Global Higher Education (CEGHE), which has been funded initially for five years by the Economic and Social Research Council (ESRC), now fills that gap.

On behalf of the ESRC CEGHE conducts and disseminates research on all aspects of higher education (HE), in order to enhance student learning and the contributions of Higher Education Institutions (HEIs) to their communities; develop the economic, social and global engagement of and impacts of UK HE; and provide data resources and advice for government and stakeholder organisations in HE in the four nations of the UK and worldwide. CEGHE is organised in three closely integrated research programmes that are focused respectively on global, national-system and local aspects of HE. CEGHE's team of researchers work on roblems and issues with broad application to the improvement of HE; develop new theories about and ways of researching HE and its social and economic contributions; and respond also to new issues as they arise, within the framework of its research programmes. An important part of CEGHE's work is the preparation and provision of data, briefings and advice to national and international policy makers, for HEIs themselves, and for UK organisations committed to fostering HE and its engagement with UK communities and stakeholders. CEGHE's seminars and conferences are open to the public and it is dedicated to disseminating its research findings on a broad basis through published papers, media articles and its website and social media platform.

CEGHE is led by Professor Simon Marginson, one of the world's leading researchers on higher education matters with a special expertise in global and international aspects of the sector. It works with partner research universities in Sheffield, Lancaster, Ireland, Australia, South Africa, Netherlands, China, Hong Kong SAR, Japan and USA. Among the issues currently the subject of CEGHE research projects are inquiries into ways and means of measuring and enhancing HE's contribution to the public good, university-industry collaboration in research, the design of an optimal system of tuition loans, a survey of the effects of tuition debt on the life choices of graduates such as investment in housing and family formation, the effects of widening participation on social opportunities in HE especially for under-represented social groups, trends and developments in HE in Europe and East Asia and the implications for UK HE, the emergence of new HE providers in the private and FE sectors, the future academic workforce in the UK and the skills that will be needed, student learning and knowledge in science and engineering, and developments in online HE.

The New York Times is releasing a series of data files with cumulative counts of coronavirus cases in the United States, at the state and county level, over time. We are compiling this time series data from state and local governments and health departments in an attempt to provide a complete record of the ongoing outbreak.

Since late January, The Times has tracked cases of coronavirus in real time as they were identified after testing. Because of the widespread shortage of testing, however, the data is necessarily limited in the picture it presents of the outbreak.

We have used this data to power our maps and reporting tracking the outbreak, and it is now being made available to the public in response to requests from researchers, scientists and government officials who would like access to the data to better understand the outbreak.

The data begins with the first reported coronavirus case in Washington State on Jan. 21, 2020. We will publish regular updates to the data in this repository.

The coronavirus disease (COVID-19) spread rampantly around the world at the beginning of 2020 before the governments of each country could prevent it by making decisions based on medical data analysis. With proper formalization, the terabytes of new textual data available online every day could have been used for the early description and detection of cases of this virus. Since then, the number of Event-Based Surveillance (EBS) applications has increased exponentially. These applications aim to mine channels of unstructured data to detect signs of possible public health events. However, one problem with such systems is the need for expert intervention to define which event will be captured, which relevant terms should be used in the search, and to analyze the events to modify the search procedure constantly. Another problem is that many of these applications do not consider both spatial and temporal characteristics. Addressing such limitations, this article presents a novel approach. We propose the biomedical domain specialization of the Core Propagation Phenomenon Ontology (PropaPhen) to capture spatiotemporal characteristics of the propagation of health-related phenomena. We also propose the Description-Detection-Framework (DDF), which leverages PropaPhen, UMLS, and OpenStreetMaps to detect new medical events automatically. Finally, we demonstrate a use case with experiments on extracts from online newspapers about COVID-19. The results show that DDF can be useful for detecting clusters of suspicious cases of possible emerging health-related phenomena.

BioPropaPhenKG, its ontology and other useful information can be found in https://zenodo.org/records/10911980. The code used for this use case can be found in https://github.com/Gabriel382/DDPF-Health-Risks . Finally, the datasets used where UMLS MetamorphoSys, OpenStreetMaps, Wikidata, Aylien (data only from November of 2019).

To read, you just need to load it with Neo4j:4.4.3. Alternatively, you can open it with docker using the following command:

docker run --interactive --tty --rm \
--publish=7474:7474 --publish=7687:7687 \
--volume=/path-to-data-folder:/data --user="$(id -u):$(id -g)"\
neo4j:4.4.3 \
neo4j-admin load --from=/data/BioPropaPhenKG-Journal-MultiRE.dump --database "neo4j" --force

Abstract

Private schools that rely entirely on student fees for financing are increasingly popular in many low-income countries and parents often prefer these schools to government-run ones. In Pakistan, children in these schools tend to outperform students in government-run schools. But financial constraints can limit the growth of these private schools, whose fees are set low to attract poor students, especially if they cannot access formal credit markets. Researchers from Pomona College, Harvard University and the World Bank have designed an impact evaluation to study private financing models - grants and loans - to support private schools in Pakistan.

The intervention centered on two financing approaches: a grant model and microloans. The program included a pilot microloan intervention to allow researchers to better develop and target loan products. This randomized control trial covered about 2,000 schools in about 650 villages across two districts in Punjab, Pakistan's most populous province.

Baseline, midline and endline surveys were conducted at both school and individual level. Within each survey, there were specific sections aimed to collect information from different perspectives. Thus, the survey initially included sections to be answered by the school owner, head teacher, class teacher, children, and operational head of the school. However, during the implementation process some changes were made in consultation with the World Bank's Strategic Impact Evaluation Fund (SIEF) and other parties involved. As a result, the final evaluation (or the endline survey) for this project shifted its focus to more specific objectives, concentrating on certain sections of initial surveys but also including additional components that would serve to the development of other projects.

The replication files for the associated American Economic Review (AER) Journal publication - Upping the Ante: The Equilibrium Effects of Unconditional Grants to Private Schools ("https://www.aeaweb.org/articles?id=10.1257/aer.20180924") are documented here for public use.

Geographic coverage

Rural areas of the district of Faisalabad in Punjab province.

Analysis unit

• schools
• individuals

Universe

The target population is low-cost private schools in rural areas of Faisalabad district. Sampling is at the village level, so urban, and peri-urban villages are excluded. Furthermore, for the design of the intervention, villages without any private schools, or with only one private school, are excluded. Villages with population over 10,000 or high village aggregated revenue are also excluded.

Government schools, and schools where money is not contained within the school itself (i.e. some network schools share money across multiple schools in the network), are also excluded.

Kind of data

Sample survey data [ssd]

Sampling procedure

All eligible schools that consented to participate across the 266 villages are included in the final randomization sample for the study. This includes 822 private and 33 NGO schools, for a total of 855 schools; there were 25 eligible schools (about 3 percent) that refused to participate in either the ballot or the surveys. The reasons for refusals were: impending school closure, lack of trust, survey burden, etc. Appendix Figure A1 of the Online Appendix (https://www.aeaweb.org/content/file?id=13118) summarizes the number of villages and schools in each experimental group.

Mode of data collection

Face-to-face [f2f]

Research instrument

Village Listing: This survey collects identifying data such as school names and contact numbers for all public and private schools in our sampling frame.

School Survey Long: This survey is administered twice, once at baseline in summer 2012 and again after treatment in the first follow-up round in May 2013. It contains two modules: the first module collects detailed information on school characteristics, operations and priorities; and the second module collects household and financial information from school owners. The preferred respondent for the first module is the operational head of the school, i.e. the individual managing day-to-day operations; if this individual was absent the day of the survey, either the school owner, the principal or the head teacher could complete the survey. For the second module, the preferred respondent was either the legal owner or the financial decision-maker of the school. In practice, the positions of operational head or school owner are often filled by the same individual.

School Survey Short: This survey is administered quarterly between October 2013 and December 2014, for a total of four rounds of data. Unlike the long school survey, this survey focuses on our key outcome variables: enrollment, fees, revenues and costs. The preferred respondent is the operational head of the school, followed by the school owner or the head teacher. Please consult Appendix Figure A3 of the Online Appendix (https://www.aeaweb.org/content/file?id=13118) to see the availability of outcomes across rounds.

Child Tests and Questionnaire: We test and collect data from children in our sample schools twice, once at baseline and once after treatment in follow-up round 3. Tests in Urdu, English and Mathematics are administered in both rounds; these tests were previously used and validated for the LEAPS project (Andrabi et al., 2002). Baseline child tests are only administered to a randomly selected half of the sample (426 schools) in November 2012. Testing is completed in 408 schools for over 5000 children, primarily in grade 4. If a school had zero enrollment in grade 4 however, then the preference ordering of grades to test was grade 3, 5, and then 6. A follow-up round of testing was conducted for the full sample in January 2014. We tested two grades between 3 and 6 at each school to ensure that zero enrollment in any one grade still provided us with some test scores from every school. From a roster of 20,201 enrolled children in this round, we tested 18,376 children (the rest were absent). For children tested at baseline, we test them again in whichever grade they are in as long as they were enrolled at the same school. We also test any new children that join the baseline test cohort. In the follow-up round, children also complete a short survey, which collects family and household information (assets, parental education, etc.), information on study habits, and self-reports on school enrollment.

Teacher Rosters: This survey collects teacher roster information from all teachers at a school. Data include variables such as teacher qualifications, salary, residence, tenure at school and in the profession. It was administered thrice during the project period, bundled with other surveys. The first collection was combined with baseline child testing in November 2012, and hence data was collected from only half of the sample. Two follow-up rounds with the full sample took place in May 2013 (round 1) and November 2014 (round 5).

Investment Plans: These data are collected once from the treatment schools as part of the disbursement activities during September-December 2012. The plans required school owners to write down their planned investments and the expected increase in revenues from these investments— whether through increases in enrollment or fees. School owners also submitted a desired disbursement schedule for the funds based on the timing of their investments.

Studies examining factors responsible for COVID-19 incidence are mostly focused at the national or sub-national level. A global-level characterization of contributing factors and temporal trajectories of disease incidence is lacking. Here we conducted a global-scale analysis of COVID-19 infections to identify key factors associated with early disease incidence. Additionally, we compared longitudinal trends of COVID-19 incidence at a per-country level and classified countries based on COVID-19 incidence trajectories and effects of lockdown responses. Univariate analysis identified eleven variables as independently associated with COVID-19 infections at a global level (p<1e-05). Multivariable analysis identified a 4-variable model as optimal for explaining global variations in COVID-19 (p<0.01). COVID-19 case trajectories for most countries were best captured by a log-logistic model, as determined by AIC estimates. Six predominant country clusters were identified when characterizing the effects of lockdown intervals on variations in COVID-19 new cases per country. Globally, economic and meteorological factors are important determinants of early COVID-19 incidence. Analysis of longitudinal trends and lockdown effects on COVID-19 highlights important nuances in country-specific responses to infections. These results provide valuable insights into disease incidence at a per-country level, possibly allowing for more informed decision making by individual governments in future disease outbreaks. Methods Data for COVID-19 confirmed cases was obtained from https://ourworldindata.org/coronavirus-source-data, which is updated daily and based on data on confirmed cases and deaths from Johns Hopkins University. Data on additional demographic, meteorological, health or economic variables were downloaded from a variety of sources online. For each variable, values from the most recent year for which data on the greatest number of countries were available were utilized (varied between 2016-2019). Variables were categorized as Demographic, Meterological, Health or Economic domains. Please see the README document ("README_data_COVID19_112322.txt") and the accompanying published article: Ghosh, S., Roy, S.S. Global-scale modeling of early factors and country-specific trajectories of COVID-19 incidence: a cross-sectional study of the first 6 months of the pandemic. BMC Public Health 22, 1919 (2022). https://doi.org/10.1186/s12889-022-14336-w

Local and global weights for criteria and sub-criteria considering the 18-25 age group.

Replication materials for "Gender System and Corruption: Patriarchy as a Predictor of ‘Fairness’," Governance (Forthcoming). doi: 10.1111/gove.12693
./sgcAnalysis.Rproj is the RStudio project file ./Analysis/sgc_WVSAnlysis generates all tables for the article and online appendix; generated files can be found in ./Analysis/Figures ./Analysis/Figures is pre-populated with two vector-image figures and one table that can not be readily drawn by R. ./Data/DoneData contains the datasets needed to replicate all tables and figures; the datasets are subsets of the World Values Survey, UNDP Human Development Index, and the Quality of Government dataset.

Dataset References: Teorell, Jan, Aksel Sundström, Sören Holmberg, Bo Rothstein, Natalia Alvarado Pachon & Cem Mert Dalli. 2021. The Quality of Government Standard Dataset, version Jan21. University of Gothenburg: The Quality of Government Institute, http://www.qog.pol.gu.se doi:10.18157/qogstdjan21 Transparency International. 2017. http://www.transparency.org/research/gcb/overview United Nations Development Programme. 2020. Human Development Reports Data Center. https://hdr.undp.org/en/data Inglehart, R., C. Haerpfer, A. Moreno, C. Welzel, K. Kizilova, J. Diez-Medrano, M. Lagos, P. Norris, E. Ponarin & B. Puranen (eds.). 2021. World Values Survey: All Rounds - Country-Pooled Datafile. Madrid, Spain & Vienna, Austria: JD Systems Institute & WVSA Secretariat.

During the COVID-19 pandemic, hospitals were challenged to provide both COVID-19 and non-COVID treatment. A survey questionnaire was designed and distributed via email to hospitals empanelled under the Ayushman Bharat–Pradhan Mantri Jan Arogya Yojana(AB-PMJAY), the world’s largest National Health Insurance Scheme. Telephonic follow-ups were used to ensure participation in places with inadequate internet. We applied support vector regression to quantify the hospital variables that affected the use vs. non-use of hospital services (Model-1), and factors impacting COVID-19 revenue and staffing levels (Model-2).We quantified the statistical significance of important input variables using Fisher’s exact test. The survey, conducted early in the pandemic, included 461 hospitals across 20 states and union territories. Only 55.5% of hospitals were delivering emergency care, 26.7% were doing elective surgery and 36.7% providing obstetric services. Hospitals with adequate supplies of PPE, including N95 masks, and separate facilities designated for COVID-19 patients were more likely to continue providing emergency surgeries and services effectively. Data analysis revealed that large hospitals (> 250 beds) with adequate PPE and dedicated COVID-19 facilities continued both emergency and elective surgeries. Public hospitals were key in pandemic management, large private hospital systems were more likely to conduct non-COVID-19 surgeries, with not-for-profit hospitals performing slightly better. Public and large private not-for-profit hospitals faced fewer staff shortages and revenue declines. In contrast, smaller hospitals (< 50 beds) experienced significant staff attrition due to anxiety, stress and revenue losses. They requested government support for PPE supplies, staff training, testing kits, and special allowances for healthcare workers. The inclusion of COVID-19 coverage under AB-PMJAY improved access to healthcare for critical cases. Maintaining non-COVID-19 care during the pandemic indicates healthcare system resiliency. A state-wide data-driven system for ventilators, beds, and funding support for smaller hospitals, would improve patient care access and collaboration.

AbstractWe collapse as a society when we subsume to a capitalist state of apathetic consumerism. Our collective apathy is a contemporaneous colonialism and when we care only enough to re-share a post in online social media of the atrocities happening in our society, in our prisons, in our communities then we have remained assimilated, mute. As hordes of the population trample gardens in the pursuit of Google controlled Pokemon's, as we push ahead in the queue for sauce smothered hot dogs at the Lego convention, there is a failure, a glitch in the reality fabric of our virtual addicted society if the 2016 Don Dale detention saga that continues to unfurl is seen as an isolated incident.

Consumer spending across India amounted to 24.57 trillion rupees by the end of the second quarter of 2024. It reached an all-time high during the fourth quarter of 2023. What is consumer spending? Consumer spending refers to the total money spent on final goods and services by individuals and households in an economy. It is an important metric that directly impacts the GDP of a country. Items that qualify as consumer spending include durable and nondurable goods and services. Various factors such as debt held by consumers, wages, supply and demand, taxes, and government-based economic stimulus can impact consumer spending in a country. Positive consumer outlook in India India’s consumer spending reflects a positive outlook with renewed consumer confidence post-COVID. Its consumer market is set to become one of the largest in the world as the number of middle- to high-income households rises with increasing amounts of disposable incomes. The country’s young demographic is also considered a driving force for increased consumer spending. Consumer electronics such as smartphones, laptops, and gaming consoles were the preferred items among Indian holiday shoppers in 2023.