scholarly journals COVID-19s Impact on the Hispanic Community: How Understanding Culture Can Improve Outcomes

2020 ◽  
Vol 3 ◽  
Author(s):  
Yamilet Guerra ◽  
Brenda Hudson
Keyword(s):  
Minority Groups ◽  
Care Delivery ◽  
Grey Literature ◽  
Past Research ◽  
Collective Good ◽  
Essential Information ◽  
Hispanic Community ◽  
Hispanic Patients ◽  
The Difference ◽  
Incidence Of Hospitalization

Background   A disproportionate burden of SARS-Cov-2 infection, or coronavirus disease 2019 (Covid-19), and death are highest among racial and ethnic minority groups. Based on data available on June 12, 2020, Hispanic people are more likely to acquire COVID-19 and have higher incidence of hospitalization and death compared to their white, non-Hispanic counterparts. While this issue is complex, many have hypothesized that the difference is due to societal factors and communication methods. The aim of this project was to evaluate information related to how the Hispanic population is affected by COVID-19 and how communications about the disease should be designed based on past research, physician input, and cultural sensitivities.    Methods  We conducted a thorough literature search on COVID-19 articles, both peer reviewed and grey literature, evaluating race and ethnic differences in disease prevalence and severity. Additionally, we conducted interviews with a small number of Indiana doctors who treat Hispanic patients to obtain a doctor’s perspective on the Hispanic community’s needs during the pandemic and ways to help reduce prevalence.    Results  Physicians in Indiana believe the main approach to help the Hispanic community is by utilizing trusted community resources to communicate information and build relationships with patients over time. It is recommended to develop new methods to deliver essential information about COVID-19 through multiple mediums, in a clear way, and in Spanish with focus on the collective good of the family.  In addition, it is important not to just translate resources from English to Spanish but to design materials addressing barriers specific to the Hispanic community.    Conclusion   More culturally tailored information should be released to educate the Hispanic community about COVID-19. This information will assist in the design of materials and initiatives for the Hispanic community that we hope will improve methods of communication and care delivery related to COVID-19.  

SSHAP In-Focus: COVID-19, Uncertainty, Vulnerability and Recovery in India

2020 ◽  
Author(s):  
Justin Pickard ◽  
Shilpi Srivastava ◽  
Mihir R. Bhatt ◽  
Lyla Mehta
Keyword(s):  
Rural Communities ◽  
Migrant Workers ◽  
Disaster Response ◽  
Minority Groups ◽  
Grey Literature ◽  
Mass Movement ◽  
Community Response ◽  
Vulnerable Groups ◽  
Ongoing Research ◽  
Informal Workers

This paper addresses COVID-19 in India, looking at how the interplay of inequality, vulnerability, and the pandemic has compounded uncertainties for poor and marginalised groups, leading to insecurity, stigma and a severe loss of livelihoods. A strict government lockdown destroyed the incomes of farmers and urban informal workers and triggered an exodus of migrant workers from Indian cities, a mass movement which placed additional pressures on the country's rural communities. Elsewhere in the country, lockdown restrictions and pandemic response have coincided with heatwaves, floods and cyclones, impeding disaster response and relief. At the same time, the pandemic has been politicised to target minority groups (such as Muslims, Dalits), suppress dissent, and undermine constitutional values. The paper focuses on how COVID-19 has intersected with and multiplied existing uncertainties faced by different vulnerable groups and communities in India who have remained largely invisible in India's development story. With the biggest challenge for government now being to mitigate the further fall of millions of people into extreme poverty, the brief also reflects on pathways for recovery and transformation, including opportunities for rural revival, inclusive welfare, and community response. This brief is based on a review of existing published and grey literature, and 23 interviews with experts and practitioners from 12 states in India, including representation from domestic and international NGOs, and local civil society organisations. It was developed for the Social Science in Humanitarian Action Platform (SSHAP) by Justin Pickard, Shilpi Srivastava, Lyla Mehta (IDS), and Mihir R. Bhatt. Some of the cases draw on ongoing research of the TAPESTRY project, which explores bottom-up transformations in marginal environments across India and Bangladesh.

scholarly journals Retweet Prediction Based on User Behavior

2021 ◽  
Author(s):  
Syeda Nadia Firdaus
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Network ◽  
Prediction Model ◽  
Matrix Factorization ◽  
Information Diffusion ◽  
User Behavior ◽  
Past Research ◽  
The Difference ◽  
The Impact

Social network is a hot topic of interest for researchers in the field of computer science in recent years. These social networks such as Facebook, Twitter, Instagram play an important role in information diffusion. Social network data are created by its users. Users’ online activities and behavior have been studied in various past research efforts in order to get a better understanding on how information is diffused on social networks. In this study, we focus on Twitter and we explore the impact of user behavior on their retweet activity. To represent a user’s behavior for predicting their retweet decision, we introduce 10-dimentional emotion and 35-dimensional personality related features. We consider the difference of a user being an author and a retweeter in terms of their behaviors, and propose a machine learning based retweet prediction model considering this difference. We also propose two approaches for matrix factorization retweet prediction model which learns the latent relation between users and tweets to predict the user’s retweet decision. In the experiment, we have tested our proposed models. We find that models based on user behavior related features provide good improvement (3% - 6% in terms of F1- score) over baseline models. By only considering user’s behavior as a retweeter, the data processing time is reduced while the prediction accuracy is comparable to the case when both retweeting and posting behaviors are considered. In the proposed matrix factorization models, we include tweet features into the basic factorization model through newly defined regularization terms and improve the performance by 3% - 4% in terms of F1-score. Finally, we compare the performance of machine learning and matrix factorization models for retweet prediction and find that none of the models is superior to the other in all occasions. Therefore, different models should be used depending on how prediction results will be used. Machine learning model is preferable when a model’s performance quality is important such as for tweet re-ranking and tweet recommendation. Matrix factorization is a preferred option when model’s positive retweet prediction capability is more important such as for marketing campaign and finding potential retweeters.

scholarly journals Retweet Prediction Based on User Behavior

2021 ◽  
Author(s):  
Syeda Nadia Firdaus
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Network ◽  
Prediction Model ◽  
Matrix Factorization ◽  
Information Diffusion ◽  
User Behavior ◽  
Past Research ◽  
The Difference ◽  
The Impact

Social network is a hot topic of interest for researchers in the field of computer science in recent years. These social networks such as Facebook, Twitter, Instagram play an important role in information diffusion. Social network data are created by its users. Users’ online activities and behavior have been studied in various past research efforts in order to get a better understanding on how information is diffused on social networks. In this study, we focus on Twitter and we explore the impact of user behavior on their retweet activity. To represent a user’s behavior for predicting their retweet decision, we introduce 10-dimentional emotion and 35-dimensional personality related features. We consider the difference of a user being an author and a retweeter in terms of their behaviors, and propose a machine learning based retweet prediction model considering this difference. We also propose two approaches for matrix factorization retweet prediction model which learns the latent relation between users and tweets to predict the user’s retweet decision. In the experiment, we have tested our proposed models. We find that models based on user behavior related features provide good improvement (3% - 6% in terms of F1- score) over baseline models. By only considering user’s behavior as a retweeter, the data processing time is reduced while the prediction accuracy is comparable to the case when both retweeting and posting behaviors are considered. In the proposed matrix factorization models, we include tweet features into the basic factorization model through newly defined regularization terms and improve the performance by 3% - 4% in terms of F1-score. Finally, we compare the performance of machine learning and matrix factorization models for retweet prediction and find that none of the models is superior to the other in all occasions. Therefore, different models should be used depending on how prediction results will be used. Machine learning model is preferable when a model’s performance quality is important such as for tweet re-ranking and tweet recommendation. Matrix factorization is a preferred option when model’s positive retweet prediction capability is more important such as for marketing campaign and finding potential retweeters.

scholarly journals The COVID-19 pandemic shifted the Veterans Affairs System toward being a payer and virtual care provider: is it sustainable?

2021 ◽  
Author(s):  
Liam Rose ◽  
Linda Diem Tran ◽  
Steven M Asch ◽  
Anita Vashi
Keyword(s):  
Financial Incentives ◽  
Care Delivery ◽  
Data Extraction ◽  
Medical Personnel ◽  
Extraction Methods ◽  
Virtual Care ◽  
Large Expansion ◽  
Virtual Visits ◽  
The Difference ◽  
One Year

Objective: To examine how VA shifted care delivery methods one year into the pandemic. Study Setting: All encounters paid or provided by VA between January 1, 2019 and February 27, 2021. Study Design: We aggregated all VA paid or provided encounters and classified them into community (non-VA) acute and non-acute visits, VA acute and non-acute visits, and VA virtual visits. We then compared the number of encounters by week over time to pre-pandemic levels. Data Extraction Methods: Aggregation of administrative VA claims and health records. Principal Findings: VA has experienced a dramatic and persistent shift to providing virtual care and purchasing care from non-VA providers. Before the pandemic, a majority (63%) of VA care was provided in-person at a VA facility. One year into the pandemic, in-person care at VA's constituted just 33% of all visits. Most of the difference made up by large expansions of virtual care; total VA provided visits (in person and virtual) declined (4.9 million to 4.2 million) while total visits of all types declined only 3.5%. Community provided visits exceeded prepandemic levels (2.3 million to 2.9 million, +26%). Conclusion: Unlike private health care, VA has resumed in-person care slowly at its own facilities, and more rapidly in purchased care with different financial incentives a likely driver. The very large expansion of virtual care nearly made up the difference. With a widespread physical presence across the U.S., this has important implications for access to care and future allocation of medical personnel, facilities, and resources.

Abstract 13150: Racial/Ethnic Disparities of the Impact of the COVID-19 Pandemic Out-of-Hospital Cardiac Arrest (OHCA) in Texas

Circulation ◽  
2021 ◽  
Vol 144 (Suppl_2) ◽  
Author(s):  
Summer Chavez ◽  
Ryan Huebinger ◽  
Kevin Schulz ◽  
Hei Kit Chan ◽  
Micah Panczyk ◽  
...  
Keyword(s):  
Cardiac Arrest ◽  
Hospital Admission ◽  
Minority Groups ◽  
Ethnic Disparities ◽  
Mixed Effect ◽  
Neurological Outcomes ◽  
Random Intercept ◽  
Hispanic Patients ◽  
The Impact ◽  
Racial Ethnic

Introduction: Prior research shows a greater disease burden, lower BCPR rates, and worse outcomes in Black and Hispanic patients after OHCA. The CDC has declared that the COVID-19 pandemic has disproportionately affected many racial and ethnic minority groups. However, the influence of the COVID-19 pandemic on OHCA incidence and outcomes in different races and ethnicities is unknown. Purpose: To describe racial/ethnic disparities in OHCA incidence, processes of care and outcomes in Texas during the COVID-19 pandemic. Methods: We used data from the Texas Cardiac Arrest Registry to Enhance Survival (CARES) comparing adult OHCA from the pre-pandemic period (March 11 - December 31, 2019) to the pandemic period (March 11- December 31, 2020). The racial and ethnic categories were White, Black, Hispanic or Other. Outcomes were rates of BCPR, AED use, sustained ROSC, prehospital termination of resuscitation (TOR), survival to hospital admission, survival to discharge and good neurological outcomes. We fit a mixed effect logistic regression model, with EMS agency designated as the random intercept to obtain aORs. We adjusted for the pandemic and other covariates. Results: A total of 8,070 OHCAs were included. The proportion of cardiac arrests increased for Blacks (903 to 1, 113, 24.9% to 25.5%) and Hispanics (935 to 1,221, 25.8% to 27.5%) and decreased for Whites (1 595 to 1,869, 44.0% to 42.1%) and Other (194 to 220, 5.4% to 5.0%) patients. Compared to Whites, Black (aOR = 0.73, 95% CI 0.65-0.82) and Hispanic patients (aOR = 0.78, 95% CI 0.68-0.87) were less likely to receive BCPR. Compared to Whites, Blacks were less likely to have sustained ROSC (aOR = 0.81, 95% CI 0.70-0.93%), with lower rates of survival to hospital admission (aOR = 0.87, 95% CI 0.75-1.0), and worse neurological outcomes (aOR = 0.45, 95% 0.28-0.73). Hispanics were less likely to have prehospital TOR compared to Whites (aOR = 0.86, 95% CI = 0.75-0.99). The Utstein bystander survival rate was worse for Blacks (aOR = 0.72, 95% CI 0.54-0.97) and Hispanics (aOR = 0.71, 95% 0.53-0.95) compared to Whites. Conclusion: Racial and ethnic disparities persisted during the COVID-19 pandemic in Texas.

scholarly journals Mortality of ethnic minority groups in the UK: a systematic review protocol

BMJ Open ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. e034903
Author(s):  
Fiona Stanaway ◽  
Naomi Noguchi ◽  
Erin Mathieu ◽  
Saman Khalatbari-Soltani ◽  
Raj Bhopal
Keyword(s):  
Systematic Review ◽  
Ethnic Diversity ◽  
Minority Groups ◽  
Meta Analysis ◽  
Grey Literature ◽  
Primary Data ◽  
Majority Population ◽  
White Majority ◽  
Newcastle Ottawa Scale ◽  
The Uk

IntroductionGrowing ethnic diversity in the UK has made it increasingly important to determine the presence of ethnic health inequalities. There has been no systematic review that has drawn together research on ethnic differences in mortality in the UK.MethodsAll types of observational studies that compare all-cause mortality between major ethnic groups and the white majority population in the UK will be included. We will search Medline (OvidSP), Embase (OvidSP), Scopus and Web of Science and search the grey literature through conference proceedings and online thesis registries. Searches will be carried out from inception to 2 August 2019 with no language or other restrictions. Database searches will be repeated prior to publication to identify new articles published since the initial search. We will conduct forward and backward citation tracking of identified references and consult with experts in the field to identify further publications and ongoing or unpublished studies. Two reviewers will independently screen studies and extract data. Two reviewers will independently assess the quality of included studies using the Newcastle-Ottawa Scale. If at least two studies are located for each ethnic group and studies are sufficiently homogeneous, we will conduct a meta-analysis. If insufficient studies are located or if there is high heterogeneity we will produce a narrative summary of results.Ethics and disseminationAs no primary data will be collected, formal ethical approval is not required. The findings of this review will be disseminated through publication in peer reviewed journals and conference presentations.PROSPERO registration numberCRD42019146143.

The Contribution of Student Experiences to Understanding Ethnic Differences in High-Risk Behaviors at School

2001 ◽  
Vol 26 (2) ◽  
pp. 152-163 ◽  
Author(s):  
Tricia Y. Marsh ◽  
Dewey G. Cornell
Keyword(s):  
High Risk ◽  
Risk Behaviors ◽  
Ethnic Differences ◽  
School Safety ◽  
Minority Groups ◽  
Past Research ◽  
School Experiences ◽  
Student Experiences ◽  
High Risk Behaviors ◽  
School Safety Survey

Past research reported that adolescent males from ethnic minority groups often engage in high-risk behaviors at school such as weapon possession, gang involvement, and fighting. The purpose of this study was to demonstrate that ethnic differences in high-risk behaviors might be better explained by differential school experiences. The study hypothesized that certain school experiences–-termed experiential factors–-rendered students more vulnerable to high-risk behaviors. The sample consisted of 7,848 seventh-, ninth-, and eleventh-grade students who completed a school safety survey. Logistic regression analyses revealed that student school experiences explained more variance than ethnicity. Low academic grades, observation and threat of violence, drug use, and perceived lack of adult and peer support were experiential factors associated with student involvement in high-risk behaviors. These results support an emphasis on student experiences rather than on ethnic background in understanding high-risk behaviors at school.

scholarly journals Estimation accuracy of covariance matrices when their eigenvalues are almost duplicated

2018 ◽  
Vol 7 ◽  
Author(s):  
Kantaro Shimomura ◽  
Kazushi Ikeda
Keyword(s):  
Covariance Matrix ◽  
Estimation Method ◽  
Estimation Accuracy ◽  
Essential Information ◽  
The Matrix ◽  
The Difference ◽  
Processing Techniques ◽  
Monotonic Property ◽  
Signal Processing Techniques ◽  
Special Case

The covariance matrix of signals is one of the most essential information in multivariate analysis and other signal processing techniques. The estimation accuracy of a covariance matrix is degraded when some eigenvalues of the matrix are almost duplicated. Although the degradation is theoretically analyzed in the asymptotic case of infinite variables and observations, the degradation in finite cases are still open. This paper tackles the problem using the Bayesian approach, where the learning coefficient represents the generalization error. The learning coefficient is derived in a special case, i.e., the covariance matrix is spiked (all eigenvalues take the same value except one) and a shrinkage estimation method is employed. Our theoretical analysis shows a non-monotonic property that the learning coefficient increases as the difference of eigenvalues increases until a critical point and then decreases from the point and converged to the distinct case. The result is validated by numerical experiments.

Authentic Versus Traditional Assessment: An Empirical Study Investigating the Difference in Seafarer Students' Academic Achievement

2020 ◽  
Vol 73 (4) ◽  
pp. 797-812
Author(s):  
Samrat Ghosh ◽  
Benjamin Brooks ◽  
Dev Ranmuthugala ◽  
Marcus Bowles
Keyword(s):  
Academic Achievement ◽  
Real World ◽  
Authentic Assessment ◽  
Past Research ◽  
Assessment Methods ◽  
Control Group ◽  
Multiple Choice Questions ◽  
Student Groups ◽  
Traditional Assessment ◽  
The Difference

Past research showed that traditional assessment methods that required seafarer students to construct responses based on memorisation and analysing information presented in absence of real-world contexts (e.g. oral examinations and multiple-choice questions) disengaged the students from learning. Memorising information is a lower-order cognitive ability, failure in which led to errors and low academic achievement for students. Authentic assessment methods require students to construct responses through the critical analysis of information presented in real-world contexts. Hence, this research investigated the difference in seafarer students' academic achievement (measured through scores obtained in assessment) in authentic assessment as compared with traditional assessment. Two separate and independent student groups (the ‘control’ group and ‘treatment’ group) were used for a selected unit of learning delivered at the Australian Maritime College within the Bachelor of Nautical Science degree program. Because some past researchers had defined and implemented traditional assessment methods as a single-occasion assessment, this project implemented the assessment in a summative format, as opposed to authentic assessments implemented during student preparation. Analysis of student scores revealed that the authentically assessed students were guided towards significantly higher academic achievement.

scholarly journals Net Fisheries’ Métiers in the Eastern Mediterranean: Insights for Small-Scale Fishery Management on Kalymnos Island

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1509 ◽  
Author(s):  
Kyriakoula Roditi ◽  
Dimitris Vafidis
Keyword(s):  
Mediterranean Sea ◽  
Fishery Management ◽  
Eastern Mediterranean ◽  
Market Price ◽  
Aegean Sea ◽  
Small Scale ◽  
Target Species ◽  
Essential Information ◽  
Small Scale Fisheries ◽  
The Difference

Small-scale fisheries constitute an important component of coastal human societies. The present study describes the small-scale net fisheries on Kalymnos Island (south-east Aegean Sea) that harbors the largest small-scale fleet in the eastern Mediterranean Sea. In addition, this study aims to evaluate their characteristics and economics. Relevant métiers were identified through a multivariate analysis by inputting the main resources and fishing gear data that were recorded during landings. Four main practices were observed being used as fishing gears, gillnets and trammel nets, targeting the species Mullus barbatus, Boops boops, Mullus surmuletus, Scorpaena porcus, and Sepia officinalis. Further analysis, which incorporated data concerning the type of the gear used, revealed 11 distinct métiers. Most of these métiers are practiced by other Mediterranean small-scale fisheries as well, in terms of target species, gear and seasonality. However, the métier that had its target species as B.boops is not practiced in other Mediterranean small-scale fisheries. The seasonal rotation of métiers was determined by the availability of different species rather than their market price. The results revealed the difference in fishing practice used by the fishermen in the study area compared to other fishing practices in the Mediterranean Sea. In particular, the fishermen of this study area targeted more species (B.boops) with a very low market price. They also provided essential information for the development and implementation of management plans aiming at the sustainability of small-scale fisheries.

Sign in / Sign up

Export Citation Format

Share Document