Relationship between sleep quality and social support in medical staff during normalized prevention and control of COVID-19.

Objective: To investigate the sleep quality and social support in medical staff during the normalized prevention and control of the COVID-19 epidemic, and to explore the relationship between them. Methods: From January to June 2021, 3163 medical personnel were recruited to participate in health screening at a tertiary general hospital. The Pittsburgh Sleep Quality Index (PSQI), Social Support Rating Scale (SSRS), Patient Health Questionnaire-2 (PHQ-2), and Generalized Anxiety Disorder Scale-2 (GAD-2) were used to evaluate sleep, social support, depression, and anxiety status, respectively. According to the SSRS scores, they could be classified as low level of social support (≤22 points), medium level of social support (23-44 points) and high level of social support (＞44 points). Results: Totally 27.8% of the participants had poor sleep quality (the PSQI score＞5 points), 90.7 % of medical staff had a moderate level of social support, with only 6.3 % having a high level. Medium and high levels of social support were negatively associated with sleep quality (OR=0.58, 0.49). Conclusion: The quality of sleep of medical staff is closely related to the level of social support during the normalized prevention and control of COVID-19. (English) [ FROM AUTHOR] 目的: 了解新冠肺炎防控常态化期间医务人员的睡眠质量和社会支持水平, 探讨两者之间的关系。方法: 2021 年 1-6 月, 招募参加某三级综合医院健康体检的医务人员 3163 名, 采用匹兹堡睡眠质量指数（PSQI）、社会支持评定量表（SSRS）、病人健康问卷-2 （PHQ-2）、广泛性焦虑障碍量表-2（GAD-2）分别调查睡眠、社会支持、抑郁和焦虑状况。根据 SSRS 得分可分为低水平社会支持（≤22 分）、中等水平社会支持（23～44 分）和高水平社会支持（＞44 分）。结果: 27.8% 的医务人员睡眠质量差（PSQI 得分＞5 分）;90.7% 的医务人员的社会支持水平为中等水平, 仅有 6.3% 为高水平。中、高水平的社会支持与睡眠质量负向关联（OR=0.58、0.49）。结论: 在新冠肺炎防控常态化期间, 医务人员的睡眠质量与社会支持水平关系密切。 (Chinese) [ FROM AUTHOR] Copyright of Chinese Mental Health Journal / Zhongguo Xinli Weisheng Zazhi is the property of Chinese Mental Health Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

COVID-19 Data Prediction and Visualization in Mainland China

Contemporarily, COVID-19 shows a sign of recurrence in Mainland China. To better understand the situation, this paper investigates the growth pattern of COVID-19 based on the research of past data through regression models. The proposed work collects the data on COVID-19 in Mainland China from January 21st, 2020, to April 30th, 2020, including confirmed, recovered, and death cases. Based on polynomial regression and support vector machine regressor, it predicts the further trend of COVID-19. The paper uses root mean squared error to evaluate the performance of both models and concludes that there is no best model due to the high frequency of daily changes. According to the analysis, support vector machine regressors fit the growth of COVID-19 confirmed case better than polynomial regression does. The best solution is to utilize different types of models to generate a range of prediction result. These results shed light on guiding further exploration of the growth of COVID-19. © 2023 SPIE.

Impact of Teachers' Support Strategies on Students' Engagement of Online College English Class Experiences

During the covid-19 pandemic, students' online learning quality is imbued with teachers' support strategies while students' learning engagement is another great indicator underlies their learning experiences. Through a questionnaire survey of 500 freshmen who have had their college English class online in 2022 fall, an investigation using exploratory factor analysis, Pearson correlation analysis, stepwise regression analysis and parallel mediator model reveals the impact of teachers' support strategies (the six dimensions of challenge, authentic context, curiosity, autonomy, recognition and feedback) on the learners' online college English learning engagement (the four dimensions of cognitive engagement, behavioral engagement, emotional engagement, social engagement), thus particular concern is also given to the correlation with students' online learning experiences. It was found that even under diversified and comprehensive guiding strategies from teachers, university students' online college English learning engagement is at the medium level, among which the cognitive engagement should be devoted more. The experimental data also shows that teachers' support strategies have significant influence on learners' engagement, especially teachers' feedback and challenge setting will stimulate students to involve more in their study. In addition, both teachers' support strategies and students' learning engagement involves significant reflection of learning experiences accordingly. Based on this learning concept, related proposals see different degrees of prominence reflected in online instructional design, teachers' and students' feedback literacy, and technology-enabled innovative teaching practice are put forward, in order to effectively play the role of teacher scaffolding, learning experiences enrichment and students' engagement enhancement of online English learning. © 2023 IEEE.

Factors Affecting Adaptability of International Students in Malaysia during COVID-19 Pandemic

It is crucial to investigate adaptability in the context of COVID-19, as evidence suggests that difficulties posed by adaptability can be exacerbated during times of crisis. International students encounter additional pressures during this period, which might impair their capacity to stay and prosper in a new place. In light of this context, the purpose of this study is to examine the factors that contribute to the adaptability of international university students in Malaysia, namely the academic system, acculturation, and social support. A cross-sectional research design was used, and the research instruments were adapted from a number of previous studies. A total of one hundred thirty online questionnaires were filled out by international students in Malaysia. The research hypotheses were evaluated using SPSS Version 27.0. All predictors were found to have a statistically significant and positive effect on the adaptability of international students. This research aims to shed light on educational management strategies for addressing the adaptability challenges faced by most international students within COVID-19 by illuminating the key drivers that influence adaptability. © Journal of Curriculum and Teaching..All rights reserved

Research on the Relationship between Teacher Expectations and College Students' Online English Learning Engagement

As a critical influencing factor of learning engagement, teacher expectation plays a vital role in ensuring the quality of online teaching under COVID-19. This paper investigates the relationship between teacher expectations (three dimensions of teacher support, teaching interaction, and academic feedback) on students' online English learning engagement (three dimensions of cognitive engagement, behavioral engagement, and emotional engagement) in e-learning through a questionnaire survey of 513 college students. Pearson correlation analysis and multiple regression analysis were applied as research methods. The results manifest that college students' online English learning engagement was above average, but emotional engagement needs improvement. In addition, teacher expectations of teaching interaction positively and significantly predict English e-learning engagement. Based on this, the article puts forward suggestions on the future of online teaching from the aspects of online teaching design, feedback quality of teachers and students, innovative teaching practice of technology empowerment to effectively play the role of teachers as scaffolding and improve the effectiveness of online English teaching. © 2023 IEEE.

Virtual Platform Adaptation of Perinatal Depression Support Group: A Quality Improvement Study

INTRODUCTION: Group therapy interventions have shown to improve scores on postpartum depression and anxiety inventories. Our institution underwent transition from in-person (IP) to virtual platform (VP) over the course of the COVID-19 pandemic. The study's objective is to examine whether VP has similar participation rates and outcomes as IP. METHODS: This is a retrospective study of women who attended the perinatal mood disorders (PMD) support group. Between January 2019 and June 2021, the group transitioned from IP to VP sessions. Participants attending both IP and VP sessions were excluded. The participation rates and outcomes of IP and VP support groups were compared. Edinburgh Postnatal Depression Score (EPDS) and Mills Inventory (MI) were used to assess clinical improvement. P value of.05 is considered significant. RESULTS: One hundred thirty-seven women (85 [62%] IP and 52 [38%] VP) participated. Baseline characteristics were similar (P >.05). Most of the participants were 1–6 months postpartum (P =.38). The average number of sessions attended by IP was 3.86 versus 3.76 by VP (P =.41). The preintervention average EPDS scores were 15.56 in IP versus 14.76 in VP (P =.79), and MI scores were 52.48 (IP) versus 46.06 in VP (P =.07). The EPDS and MI mean scores decreased postintervention. The average decrease in EPDS score is 5.08 in IP versus 6 in VP (P =.36). The average decrease in MI score is 17.86 in IP versus 15.56 in VP (P =.25). CONCLUSION: The VP has similar participation and outcomes as IP format. The VP is a feasible and effective method of delivering support to women with PMD. [ FROM AUTHOR] Copyright of Obstetrics & Gynecology is the property of Lippincott Williams & Wilkins and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Economic development in a pandemic: Reflections from Melbourne, ‘the world's most locked-down city'

This paper explores the key principles of economic development in a pandemic. It does so by drawing on the lessons learned through the economic response to the COVID-19 pandemic in the Australian city of Melbourne. Melburnians experienced 263 days largely confined to their homes, one of the longest lockdown periods in the world, resulting in deserted city streets and a devastated central city economy. The experience forced the City of Melbourne to adopt a range of unique and innovative responses, first to keep businesses afloat during lockdown, and then to reactivate the city. Melbourne did this while keeping a focus on longer-term economic development. This somewhat unique experience makes Melbourne a useful case study informing best-practice economic development in a pandemic. Key lessons emerging from Melbourne's experience include the need to respond and iterate rapidly, the importance of collaboration with stakeholders and other tiers of government, and the need to maintain a focus on multiple time horizons, even in the midst of the crisis. These insights are potentially transferrable to economic development responses to other crises, including those catalysed by climate change. © 2023, Henry Stewart Publications. All rights reserved.

Child, adolescent, and parent mental health in general population during a year of COVID-19 pandemic in belgium: a cross-sectional study

Background: This study aims to evaluate the mental health status of children, adolescents and their parents during the first year of COVID-19 pandemic in Belgium. Method(s): Analysis compared results before and during the second national lockdown, which started on November 2nd 2020. A cross-sectional online survey was conducted between May 2020 and April 2021. Result(s): Two hundred and eighteen adults and 273 children fully completed the survey. Almost one in five children (17.9%) presented moderate-to-severe scores of depression. Adolescents presented a higher level of depression than children (p = 0.007). The rate of moderate-to-severe depression scores (10.8% to 21%, p = 0.007) and internalized symptoms increased during the second lockdown (p < 0.001). Parents' depression (p < 0.001) and anxiety (p = 0.027) levels also increased during the second lockdown. Logistic regression showed that the use of psychotropic medication in parents and parents' depression scores were risk factors for children to have worse depression scores. Conclusion(s): The second lockdown appears to worsen the effects of the pandemic on children's and parents' mental health. There is a need to implement specific interventions targeting both children/adolescents and their parents to support them during lockdown periods and improve mental health outcomes.Copyright © 2022, The Author(s).

Impact of Pandemic Outbreak and Targeted Required Reserve Ratio Cut Policy on Stock Return Rate: Evidence from China

This study analyzed the impact of COVID-19 outbreak and targeted required reserve ratio cut policy on stock returns of Chinese listed companies. This paper uses the data of 3,449 A-share listed companies from February 3, 2020 to December 31, 2020 for research, the empirical results showed that stock prices of private enterprises with stronger debt-paying ability and looser financing constraints, and state-owned enterprises with less supply chain credit risks performed better, in the central and western regions, enterprises with stronger solvency and looser financing constraints have better stock price performance during the early stages of pandemic. After the implementation of the targeted RRR cut policy, the stock prices of enterprises with poor solvency, private enterprises, and enterprises in central and western regions with strong financing constraints, state-owned enterprises, and enterprises in eastern region with high credit risks all showed significant reversals, and the stock prices reflected the effect of the targeted RRR cut policy in the short and medium term. Over time, the pandemic has been controlled, and the resumption of work and production has freed most enterprises from financial difficulties. However, due to sporadic outbreaks, large private enterprises and eastern enterprises with strong risk resistance and loose financing constraints enjoy better stock price performance. This study is helpful for enterprises to understand the value of financial flexibility and solvency and provides a reference for enterprises to make financial decisions: how to balance the benefits and costs of solvency. © Tian Wang, Fang Fang and Linhao Zheng, 2023.

Reassures Me There Are People Around Who Care about Me: Analyzing Meanings of Support for People with Epilepsy

Aims The study aimed to examine the nature and outcomes of social support for the well-being and quality of life (QoL) of adults with epilepsy. Methods The Australian Epilepsy Longitudinal Survey's 5 th Wave included the Medical Outcomes Study Modified Social Support Survey on four dimensions of social support and the QOLIE-31. Both numerical data and open-ended responses were analyzed quantitatively and qualitatively. A mixed method was used where quantitative analysis used t -tests, analysis of variance and block recursive regression, and qualitative analysis identified themes. Results Three-hundred thirty-two people with epilepsy (PWE) participated. The quantitative component showed that for emotional/informational support being older and living alone were key factors. These as well as household income were important in tangible support. For affectionate support living alone and household income were factors, but only income was a factor for positive social interaction. In addition, only positive social interaction predicted increased QoL. In the qualitative component of the study both positive and negative supports were identified for the emotional/informational supports. Many reported the benefits of having information, although some reported failure to have their needs taken seriously by health professionals or epilepsy associations. Inability to drive was an important feature of negative tangible support as was finding some assistive services unaffordable. Reports of positive social interaction identified being accepted as a person with epilepsy that is the primary concern. Peer support plays a role here. Discussion The findings of factors affecting the four dimensions of support in this study have supported a number of studies. This includes the effects of positive social interaction on QoL. The qualitative analysis supports these results, but it also contributes to deeper understanding of support in the lives of PWE. Conclusion Social support is a particularly important issue for PWE. The results of this study demonstrate the complexities and benefits of obtaining the appropriate forms of support. This survey took place before the coronavirus disease 2019 pandemic and it may well be that this phenomenon affects PWE's support needs. Copyright © 2023. Indian Epilepsy Society. All rights reserved.

Parental Burnout During the Second Year of the COVID-19 Pandemic: Exploring the Role of Parenting Stressors and Coparenting Support

The COVID-19 pandemic brought about many changes in family routines and introduced new stressors for parents. While stressors can lead to parental burnout, coparenting support may mitigate the effects of parental stress on parental burnout. The current study explored the effects of parental stress, COVID-19 stress, and coparenting support on parental burnout during the second year of the pandemic. Participants consisted of one hundred fifty-five parents in the USA (M = 39.6, SD = 7.38;female = 94.8%). Results suggested parental stress was positively associated with parental burnout while coparenting support was negatively associated with parental burnout. These findings highlight the importance of addressing parental stress and support to minimize the risk of parental burnout. [ FROM AUTHOR] Copyright of Family Journal is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Understanding Mental Wellbeing amongst Potentially Vulnerable Higher Education Students during the COVID-19 Pandemic

During the COVID-19 pandemic, higher education students were reported as one of the most affected in terms of wellbeing. In the current study, we explored higher education students' self-perceptions on why and how their mental wellbeing was affected by the COVID-19 pandemic and its related measures. Focus group discussions with potentially vulnerable university student groups, as well as university staff, revealed that the students described the pandemic as a period of reduced mental wellbeing. Overall, students identified two main reasons for this: the lingering aspect of the pandemic and restricted social contact and support. Moreover, we identified several underlying factors of specific student groups' vulnerability in terms of wellbeing: entering the university during the COVID-19 pandemic and students' living situation, employment status, financial stress, and home environment. Moreover, as a potential mitigating factor, the availability of university facilities during the COVID-19 pandemic emerged as beneficial for students' mental wellbeing. Our results indicate the importance of universities acting as social spaces that facilitate connection and peer support and, thus, working preventively towards the improved mental wellbeing of students. Furthermore, university support in providing facilities to increase inclusivity should be organized or improved. © 2023 by the authors.

DEEP LEARNING BASED AN EFFICIENT HYBRID PREDICTION MODEL FOR COVID-19 CROSS-COUNTRY SPREAD AMONG E7 AND G7 COUNTRIES

The COVID-19 pandemic has caused the death of many people around the world and has also caused economic problems for all countries in the world. In the literature, there are many studies to analyze and predict the spread of COVID-19 in cities and countries. However, there is no study to predict and analyze the cross-country spread in the world. In this study, a deep learning based hybrid model was developed to predict and analysis of COVID-19 cross-country spread and a case study was carried out for Emerging Seven (E7) and Group of Seven (G7) countries. It is aimed to reduce the workload of healthcare professionals and to make health plans by predicting the daily number of COVID-19 cases and deaths. Developed model was tested extensively using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and R Squared (R2). The experimental results showed that the developed model was more successful to predict and analysis of COVID-19 cross-country spread in E7 and G7 countries than Linear Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM). The developed model has R2 value close to 0.9 in predicting the number of daily cases and deaths in the majority of E7 and G7 countries. © 2023 by the authors.

Examination of the Effects of COVID-19 on Happiness in Different Geographical Regions with Piecewise Linear Panel Data Models

The COVID-19 pandemic has recently caused the loss of millions of lives, and billions of others have been deeply affected. This crisis has changed the way people live, think about life, and perceive happiness. The aim of this study is to reveal differences between geographical regions by investigating the effect of the happiness variable on different countries during the international COVID-19 pandemic. The primary purpose is to demonstrate how such a pandemic may affect different countries in terms of happiness at the individual level and to identify possible strategies for the future. With this aim, both static and dynamic panel data models were used while applying fixed effects, random effects, and the generalized method of moments (GMM). A basic assumption in panel data models is that the coefficients do not change over time. This assumption is unlikely to hold, however, especially during major devastating events like COVID-19. Therefore, the piecewise linear panel data model was applied in this study. As a result of empirical analysis, pre- and post-COVID differences were seen between different geographical regions. Based on analysis conducted for three distinct geographical regions with piecewise linear models, it was determined that the piecewise random effects model was appropriate for European and Central Asian countries, the piecewise FGLS model for Latin American and Caribbean countries, and the piecewise linear GMM model for South Asian countries. According to the results, there are many variables that affect happiness, which vary according to different geographical conditions and societies with different cultural values.

Impact of COVID-19 on rural and nonrural recovery housing in the United States

This study aimed to assess the impact of COVID-19 on recovery housing (RH), an important resource for individuals in recovery from substance use disorder (SUD). A cross-sectional survey was disseminated electronically between June and July of 2020 to RH owners and operators affiliated with Oxford House or the National Alliance of Recovery Residences nationwide. The survey intended to develop an understanding of the impact of COVID-19 on RH in terms of (a) resident housing access, (b) mitigation strategies to reduce COVID-19 spread, (c) RH financials, and (d) health and well-being of residents and staff. Impacts were assessed among all houses in the sample and then by rurality of RH location (rural vs. nonrural). Among 1,419 respondents, only 4.6% reported positive COVID-19 cases, and 85% reported having implemented centers for disease control-recommended policies. More than half (59%) reported financial impacts, and close to half (49%) reported COVID-19 had "a lot of impact" on residents attending meetings. Rural RH represented only 9% of respondents and a greater fraction of rural RH respondents reported spending more on all COVID-19 expense categories compared to nonrural RH respondents. Compared to nonrural RH, rural RH were significantly more likely to report having a process for evaluating COVID-19 (p = .007), wearing masks (p = .047), taking temperatures (p = .042), and spending more on food due to COVID-19 (p = .015). With SUD rates and the associated morbidity and mortality from SUD continuing to rise, addressing the financial viability of RH, an important resource supporting individuals in recovery is crucial. (PsycInfo Database Record (c) 2023 APA, all rights reserved) Impact Statement This study suggests that recovery housing, an important resource for individuals seeking or in recovery from a substance use disorder (SUD), is proactive in ensuring resident safety during national emergencies such as COVID-19. The most prominent impacts found in this study were financial (for the recovery home) and residents' ability to attend mutual aid recovery support meetings. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Intelligent Methods and Models for Assessing Level of Student Adaptation to Online Learning

The problem of introducing online learning is becoming more and more popular in our society. Due to COVID-19 and the war in Ukraine, there is an urgent need for the transition of educational institutions to online learning, so this paper will help people not make mistakes in the process and afterward. The paper's primary purpose is to investigate the effectiveness of machine learning tools that can solve the problem of assessing student adaptation to online learning. These tools include intelligent methods and models, such as classification techniques and neural networks. This work uses data from an online survey of students at different levels: school, college, and university. The survey consists of questions such as gender, age, level of education, whether the student is in the city, class duration, quality of Internet connection, government/non-government educational institution, availability of virtual learning environment, whether the student is familiar with IT, financial conditions, type of Internet connection, a device used for studying, etc. To obtain the results on the effectiveness of online education were used the following machine learning algorithms and models: Random Forest (RF), Extra Trees (ET), Extreme, Light, and Simple Gradient Boosting (GB), Decision Trees (DT), K-neighbors (K-mean), Logistic Regression (LR), Support Vector Machine (SVM), Naїve Bayes (NB) classifier and others. An intelligent neural network model (NNM) was built to address the main issue. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (CEUR-WS.org)

Tweets Emotions Analysis of Community Activities Restriction as COVID-19 Policy in Indonesia Using Support Vector Machine

With the rising number of COVID-19 cases in Indonesia, the government has implemented the Imposition of Restrictions on Emergency Community Activities (Pemberlakuan Pembatasan Kegiatan Masyarakat - PPKM) as Indonesia's COVID-19 policy. Several controversies and protests have colored the implementation of this emergency policy. Some netizens on Twitter voice their opinions about the policy in their tweets. Emotions in tweets can be recognized through text-based emotion detection or emotion analysis. However, text-based emotion detection is a challenging task. One of the main issues in classifying text with a machine learning-based approach deals with the feature dimensions. As a result, appropriate methods for accurately identifying emotion based on the text are required. The research studies an emotions analysis task on Indonesians' PPKM-related tweets to understand their emotional state while implementing the PPKM. The machine learning classification algorithms used are Support Vector Machine (SVM) and random forest. The total number of tweets is 4,401. The results show that SVM with linear kernel function combined with the TF-IDF and Chi-Square methods outperforms other classifiers with an accuracy of 0.7528. The accuracy value is higher than those obtained by previous studies. Moreover, the results of the emotion classification on PPKM tweets reveal that most Indonesians are unhappy with the implementation of the PPKM policy. © 2023 Bina Nusantara University. All rights reserved.

Kaleidoscope: A Reflective Documentation Tool for a User Interface Design Course

Documentation can support design work and create opportunities for learning and reflection. We explore how a novel documentation tool for a remote interaction design course provides insight into design process and integrates strategies from expert practice to support studio-style collaboration and reflection. Using Research through Design, we develop and deploy Kaleidoscope, an online tool for documenting design process, in an upper-level HCI class during the COVID-19 pandemic, iteratively developing it in response to student feedback and needs. We discuss key themes from the real-world deployment of Kaleidoscope, including: tensions between documentation and creation;effects of centralizing discussion;privacy and visibility in shared spaces;balancing evidence of achievement with feelings of overwhelm;and the effects of initial perceptions and incentives on tool usage. These successes and challenges provide insights to guide future tools for design documentation and HCI education that scaffold learning process as an equal partner to execution. © 2023 Owner/Author.

Productive Aging During COVID-19 - Lessons from WH SeniorLink

Introduction: According to the National Academies of Science, Medicine, and Engineering, even before the pandemic, 24% of Americans 65+ were considered socially isolated, and 43% of adults 60+ were lonely. Both experiences are associated with serious physical and mental health problems, including increased risks of dementia, stroke, depression, and suicidal ideation. As older adults engage in stricter social distancing to protect themselves from COVID-19, their risk of social isolation and loneliness is heightened. According to research by the Kaiser Family Foundation, 46% of adults 65+ reported that the worry and stress caused by pandemic has had a negative impact on their mental health. In order to combat this unique challenge, we must employ innovative, flexible solutions that adapt to the shifting circumstances and an uncertain future. Method(s): WH SeniorLink has developed an innovative program for integrating friendly visiting, mutual aid, needs assessment and case management, all delivered remotely by trained volunteers. The program was conceived, developed and executed since the beginning of COVID-19 restrictions and has grown to become a fully fledged 501(c)(3) organization, and is a model of flexible service delivery during this unusual time. The program operates through a volunteerism-mutual aid model. Volunteers are trained using online modules and quizzes and supervised by a licensed social worker, including training on needs and risk assessment. Volunteers are then matched with an older adult with similar interests and language preferences to provide friendly weekly phone calls, care packages and letters. Older adults with higher needs are referred to WH SeniorLink's service navigation program which operates using the online platform, Apricot, and is staffed by masters level social work interns and supervised by licensed social workers. Service navigation involves conducting comprehensive assessments and identifying resources and referrals. Older adults who express interest are also paired with each other to provide social support. Barriers to program development have included identifying consistent funding sources and volunteer attrition. Result(s): Data shows 169% growth in contacts made with older adults between May and October, with 2.5 times more older adults served in November than in May. Initial responses to the Dejong Gierveld Loneliness Scale and qualitative data collection reflect that older adults continue to feel lonely and isolated in the midst of the pandemic, but that WH SeniorLink is helping. By placing emphasis on empowering older adults to form sustained relationships with volunteers and their peers, WH SeniorLink encourages reciprocal relationships through which older adult participants are valued for their experiences and contributions. Conclusion(s): The mission of WH SeniorLink is to strengthen community ties and improve health outcomes among older New Yorkers by providing social-emotional support and connection to essential services. WH SeniorLink was founded during the COVID-19 pandemic, in response to the myriad of new challenges faced by older adults including greater risk of social isolation and increased disconnection from essential services. However, we recognize that the pandemic has only exacerbated a problem that began long before, and older adults are at risk of being left behind in the coming months and beyond. Funding(s): WH SeniorLink is funded by a starter grant from Columbia School of Social Work and subsequent crowdfunded donations.Copyright © 2021