scholarly journals Predictors of Anxiety and Depression in Medical Professionals During the Time of COVID-19 Outbreak

2021 ◽  
pp. 1-19
Author(s):  
Zhengjia Ren ◽  
Zhongyao Xie
Keyword(s):  
Mental Health ◽  
Social Media ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Mental Health Disorder ◽  
Anxiety And Depression ◽  
Medical Professionals ◽  
Depression Symptoms ◽  
Sociodemographic Data

ABSTRACT Objectives: The aim of this study was to investigate the influences of sociodemographic data, mental disorder history, confusion and somatic discomfort triggered by social media on anxiety and depression symptoms among medical professionals during COVID-19 outbreak. Methods: 460 participants completed online questionnaires that included sociodemographic data, mental health disorder history, an assessment of confusion and somatic discomfort triggered by social media, and psychological disturbance. Hierarchical linear regression model was adopted to analysis the data. Results: The hierarchical linear regression model was able to explain 41.7% of variance in depression symptoms. Including: comorbidity with one mental disorder (B= 0.296, P < .001), confusion (B= 0.174, P < .001) and somatic discomfort (B=0.358, P<.001) triggered by social media. The hierarchical linear regression model was able to explain 41.7% of variance in anxiety symptoms, including: sex (B = -0.08, P < .005), comorbidity with one mental health disorder (B= 0.242, P < .001), confusion (B= 0.228, P < .001) and somatic discomfort (B=0.436, P<.001) triggered by social media. Conclusions: These results suggest that it is important to provide adequate psychological assistance for medical professionals with mental health problems in COVID-19 to buffer the negative impact of social media.

scholarly journals COVID-19 Related Media Consumption and Mental Health in Older Adults

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 1006-1006
Author(s):  
Erta Cenko ◽  
Christopher Kaufmann ◽  
Todd Manini
Keyword(s):  
Mental Health ◽  
Older Adults ◽  
Social Media ◽  
Depressive Symptoms ◽  
Anxiety And Depression ◽  
Media Consumption ◽  
Depression Symptoms ◽  
Negative Consequences ◽  
Cross Sectional Survey ◽  
Cross Sectional

Abstract At the beginning of the COVID-19 pandemic, consuming media was critical to identify precautionary behaviors to reduce the spread of the virus, particularly for older adults. Media consumption leads to heightened awareness, but may also negatively affect mental health. We examined whether non-social and social media consumption impacted anxiety and depression relative to pre-COVID-19 symptoms. We conducted an anonymous, cross-sectional survey in May and June 2020. Participants (n=1,168, 73.2 years, 56.8% women, 94.9% White), were asked to estimate their amount of time spent consuming pandemic-related media each day, and to report on anxiety and depressive symptoms both before and after the pandemic onset. We characterized change in anxiety and depression by subtracting scores on current anxiety and depressive symptoms from their recalled symptoms prior to the pandemic. Respondents with high pandemic-related media consumption (&gt;3hrs) were more likely to have increased anxiety, compared to those with low (&lt;1hr) media consumption (OR:1.57, 95%CI:1.09-2.23). Similarly, respondents with increased social media consumption during the pandemic were 64% more likely to have depression, compared to those who did not use social media. This association was bi-directional— those who reduced their social media use were 45% less likely to have depression and 26% less likely to have anxiety, compared to those who never used social media. Older adults consuming more pandemic-related media had increased anxiety. Increased social media consumption was associated with elevated depression symptoms. The potential benefits of media consumption about the COVID-19 pandemic may have unintended negative consequences on mental health.

scholarly journals Factors that contribute to social media influence within an Internal Medicine Twitter learning community

F1000Research ◽  
2014 ◽  
Vol 3 ◽  
pp. 120 ◽  
Author(s):  
Tejas Desai ◽  
Manish Patwardhan ◽  
Hunter Coore
Keyword(s):  
Internal Medicine ◽  
Social Media ◽  
Linear Regression ◽  
Regression Model ◽  
Faculty Member ◽  
Learning Community ◽  
Linear Regression Model ◽  
Media Influence ◽  
Multivariate Linear Regression Model ◽  
The Status

Medical societies, faculty, and trainees use Twitter to learn from and educate other social media users. These social media communities bring together individuals with various levels of experience. It is not known if experienced individuals are also the most influential members. We hypothesize that participants with the greatest experience would be the most influential members of a Twitter community.We analyzed the 2013 Association of Program Directors in Internal Medicine Twitter community. We measured the number of tweets authored by each participant and the number of amplified tweets (re-tweets). We developed a multivariate linear regression model to identify any relationship to social media influence, measured by the PageRank.Faculty (from academic institutions) comprised 19% of the 132 participants in the learning community (p < 0.0001). Faculty authored 49% of all 867 tweets (p < 0.0001). Their tweets were the most likely to be amplified (52%, p < 0.01). Faculty had the greatest influence amongst all participants (mean 1.99, p < 0.0001). Being a faculty member had no predictive effect on influence (β = 0.068, p = 0.6). The only factors that predicted influence (higher PageRank) were the number of tweets authored (p < 0.0001) and number of tweets amplified (p < 0.0001)The status of “faculty member” did not confer a greater influence. Any participant who was able to author the greatest number of tweets or have more of his/her tweets amplified could wield a greater influence on the participants, regardless of his/her authority.

Physico-Chemical Analysis of Groundwater and Effect of Climatic Change in Angul-Talcher Area

2018 ◽  
Vol 6 (12) ◽  
pp. 26
Author(s):  
Aliva Bera ◽  
D.P. Satapathy
Keyword(s):  
Water Resources ◽  
Linear Regression ◽  
Regression Model ◽  
Climatic Change ◽  
Linear Regression Model ◽  
Ann Model ◽  
Quality Of Water ◽  
Physico Chemical ◽  
Simulation Based

In this paper, the linear regression model using ANN and the linear regression model using MS Excel were developed to estimate the physico-chemical concentrations in groundwater using pH, EC, TDS, TH, HCO3 as input parameters and Ca, Mg and K as output parameters. A comparison was made which indicated that ANN model had the better ability to estimate the physic-chemical concentrations in groundwater. An analytical survey along with simulation based tests for finding the climatic change and its effect on agriculture and water bodies in Angul-Talcher area is done. The various seasonal parameters such as pH, BOD, COD, TDS,TSS along with heavy elements like Pb, Cd, Zn, Cu, Fe, Mn concentration in water resources has been analyzed. For past 30 years rainfall data has been analyzed and water quality index values has been studied to find normal and abnormal quality of water resources and matlab based simulation has been done for performance analysis. All results has been analyzed and it is found that the condition is stable. 

Prediction of Surface Roughness and Optimization of Cutting Parameters in CNC Turning of Rotational Features

2020 ◽  
Vol 38 (8A) ◽  
pp. 1143-1153
Author(s):  
Yousif K. Shounia ◽  
Tahseen F. Abbas ◽  
Raed R. Shwaish
Keyword(s):  
Surface Roughness ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Feed Rate ◽  
Cutting Parameters ◽  
Spindle Speed ◽  
Depth Of Cut ◽  
Non Linear ◽  
Optimization Of Cutting Parameters

This research presents a model for prediction surface roughness in terms of process parameters in turning aluminum alloy 1200. The geometry to be machined has four rotational features: straight, taper, convex and concave, while a design of experiments was created through the Taguchi L25 orthogonal array experiments in minitab17 three factors with five Levels depth of cut (0.04, 0.06, 0.08, 0.10 and 0.12) mm, spindle speed (1200, 1400, 1600, 1800 and 2000) r.p.m and feed rate (60, 70, 80, 90 and 100) mm/min. A multiple non-linear regression model has been used which is a set of statistical extrapolation processes to estimate the relationships input variables and output which the surface roughness which prediction outside the range of the data. According to the non-linear regression model, the optimum surface roughness can be obtained at 1800 rpm of spindle speed, feed-rate of 80 mm/min and depth of cut 0.04 mm then the best surface roughness comes out to be 0.04 μm at tapper feature at depth of cut 0.01 mm and same spindle speed and feed rate pervious which gives the error of 3.23% at evolution equation.

An Enhanced Multiple Linear Regression Model for Seasonal Rainfall Prediction

2020 ◽  
Vol 10 (4) ◽  
pp. 473-483
Author(s):  
Pundra Chandra Shaker Reddy ◽  
Alladi Sureshbabu
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Weather Prediction ◽  
Big Data Analytics ◽  
Multiple Linear Regression Model ◽  
Seasonal Rainfall ◽  
Climate Data ◽  
Rainfall Forecasting

Aims & Background: India is a country which has exemplary climate circumstances comprising of different seasons and topographical conditions like high temperatures, cold atmosphere, and drought, heavy rainfall seasonal wise. These utmost varieties in climate make us exact weather prediction is a challenging task. Majority people of the country depend on agriculture. Farmers require climate information to decide the planting. Weather prediction turns into an orientation in farming sector to deciding the start of the planting season and furthermore quality and amount of their harvesting. One of the variables are influencing agriculture is rainfall. Objectives & Methods: The main goal of this project is early and proper rainfall forecasting, that helpful to people who live in regions which are inclined natural calamities such as floods and it helps agriculturists for decision making in their crop and water management using big data analytics which produces high in terms of profit and production for farmers. In this project, we proposed an advanced automated framework called Enhanced Multiple Linear Regression Model (EMLRM) with MapReduce algorithm and Hadoop file system. We used climate data from IMD (Indian Metrological Department, Hyderabad) in 1901 to 2002 period. Results: Our experimental outcomes demonstrate that the proposed model forecasting the rainfall with better accuracy compared with other existing models. Conclusion: The results of the analysis will help the farmers to adopt effective modeling approach by anticipating long-term seasonal rainfall.

Childhood Material Hardship and Adolescent Mental Health

2021 ◽  
pp. 0044118X2110018
Author(s):  
Chrisse Edmunds ◽  
Melissa Alcaraz
Keyword(s):  
Mental Health ◽  
Adolescent Depression ◽  
Adolescent Mental Health ◽  
Anxiety And Depression ◽  
Fragile Families ◽  
Depression Symptoms ◽  
Depression And Anxiety ◽  
Material Hardship ◽  
Critical Age ◽  
The Relationship

Adolescent mental health has implications for current and future wellbeing. While a link exists between poverty and mental health, little is known about how experiencing material hardship, such as insecurity of food, housing, utilities, and medical care, throughout early childhood affects adolescent mental health. We examine the relationship between material hardship in childhood and adolescent mental health. We use Poisson regression to examine the effect of material hardship experienced at different stages of childhood on adolescent depression and anxiety outcomes at age 15. We use longitudinal data from the Fragile Families and Child Wellbeing Study ( N = 3,222). We find that recently experiencing material hardship during childhood is positively and significantly associated with anxiety and depression symptoms at age 15, even when controlling for material hardship at age 15. Additionally, we find that insecurity during mid-childhood and the stress of lacking basic needs during a critical age may influence mental health in adolescence.

scholarly journals Vitamin A and E Homologues Impacting the Fate of Acrylamide in Equimolar Asparagine-Glucose Model System

Antioxidants ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 993
Author(s):  
Su Lee Kuek ◽  
Azmil Haizam Ahmad Tarmizi ◽  
Raznim Arni Abd Razak ◽  
Selamat Jinap ◽  
Maimunah Sanny
Keyword(s):  
Vitamin E ◽  
Linear Regression ◽  
Vitamin A ◽  
Regression Model ◽  
Linear Regression Model ◽  
Model System ◽  
Concentration Increase ◽  
Acrylamide Formation ◽  
Β Carotene

This study aims to evaluate the influence of Vitamin A and E homologues toward acrylamide in equimolar asparagine-glucose model system. Vitamin A homologue as β-carotene (BC) and five Vitamin E homologues, i.e., α-tocopherol (AT), δ-tocopherol (DT), α-tocotrienol (ATT), γ-tocotrienol (GTT), and δ-tocotrienol (DTT), were tested at different concentrations (1 and 10 µmol) and subjected to heating at 160 °C for 20 min before acrylamide quantification. At lower concentrations (1 µmol; 431, 403, 411 ppm, respectively), AT, DT, and GTT significantly increase acrylamide. Except for DT, enhancing concentration to 10 µmol (5370, 4310, 4250, 3970, and 4110 ppm, respectively) caused significant acrylamide formation. From linear regression model, acrylamide concentration demonstrated significant depreciation over concentration increase in AT (Beta = −83.0, R2 = 0.652, p ≤ 0.05) and DT (Beta = −71.6, R2 = 0.930, p ≤ 0.05). This study indicates that different Vitamin A and E homologue concentrations could determine their functionality either as antioxidants or pro-oxidants.

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