scholarly journals Early Predictors of Long-Term Disability After Injury

2003 ◽  
Vol 12 (3) ◽  
pp. 197-205 ◽  
Author(s):  
Therese S. Richmond ◽  
Donald Kauder ◽  
Janice Hinkle ◽  
Justine Shults
Keyword(s):  
At Risk ◽  
Structural Equation Modeling ◽  
Psychological Distress ◽  
Structural Equation ◽  
Equation Modeling ◽  
Specific Information ◽  
Care Providers ◽  
Early Predictors ◽  
Patients At Risk

• Background Improving outcomes after serious injury is important to patients, patients’ families, and healthcare providers. Identifying early risk factors for long-term disability after injury will help critical care providers recognize patients at risk. • Objectives To identify early predictors of long-term disability after injury and to ascertain if age, level of disability before injury, posttraumatic psychological distress, and social network factors during hospitalization and recovery significantly contribute to long-term disability after injury. • Methods A prospective, correlational design was used. Injury-specific information on 63 patients with serious, non–central nervous system injury was obtained from medical records; all other data were obtained from interviews (3 per patient) during a 2½-year period. A model was developed to test the theoretical propositions of the disabling process. Predictors of long-term disability were evaluated using path analysis in the context of structural equation modeling. • Results Injuries were predominately due to motor vehicle crashes (37%) or violent assaults (21%). Mean Injury Severity Score was 13.46, and mean length of stay was 12 days. With structural equation modeling, 36% of the variance in long-term disability was explained by predictors present at the time of injury (age, disability before injury), during hospitalization (psychological distress), or soon after discharge (psychological distress, short-term disability after injury). • Conclusions Disability after injury is due partly to an interplay between physical and psychological factors that can be identified soon after injury. By identifying these early predictors, patients at risk for suboptimal outcomes can be detected.

scholarly journals Proposing a model for absorption capacity of technology

2015 ◽  
Vol 4 (1) ◽  
pp. 113 ◽  
Author(s):  
Hossein Gholizadeh ◽  
Ali Bonyadi Naeini ◽  
Alireza Moini
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Developed Countries ◽  
Absorption Capacity ◽  
Equation Modeling ◽  
Technological Gap ◽  
Expert Opinions ◽  
Modeling Analysis ◽  
Effective Dimensions

Absorption capacity of knowledge is the concept which is been introduced in this situation. This phenomenon describes why some countries are in a better condition with a view to technology. Many countries believe that the power to stay in the battle and excel other competitors directly depends on their abilities in taking knowledge. Many countries have used their technological gap with developed countries as an accelerator for improving their absorption capacity. In this study, authors tried to produce a complete definition for absorption capacity through analyzing different research. Then with using expert opinions, a categorization for dimensions of absorption capacity will be prepared by using the structural equation modeling analysis, and finally a model for measuring absorption capacity is suggested. The results show effective dimensions in 5 different section. Studies depict that the most important one is keeping which could be a fundamental for long-term policies of 1404.

scholarly journals Influences on Attitudes Regarding Potential COVID-19 Vaccination in the United States

Vaccines ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 582 ◽  
Author(s):  
Kendall Pogue ◽  
Jamie L. Jensen ◽  
Carter K. Stancil ◽  
Daniel G. Ferguson ◽  
Savannah J. Hughes ◽  
...  
Keyword(s):  
United States ◽  
Structural Equation Modeling ◽  
Side Effects ◽  
Structural Equation ◽  
The United States ◽  
Equation Modeling ◽  
Vaccine Acceptance ◽  
The World ◽  
Short And Long Term

The COVID-19 pandemic continues to ravage the world, with the United States being highly affected. A vaccine provides the best hope for a permanent solution to controlling the pandemic. However, to be effective, a vaccine must be accepted and used by a large majority of the population. The aim of this study was to understand the attitudes towards and obstacles facing vaccination with a potential COVID-19 vaccine. To measure these attitudes a survey was administered to 316 respondents across the United States by a survey corporation. Structural equation modeling was used to analyze the relationships of several factors with attitudes toward potential COVID-19 vaccination. Prior vaccine usage and attitudes predicted attitudes towards COVID-19 vaccination. Assessment of the severity of COVID-19 for the United States was also predictive. Approximately 68% of all respondents were supportive of being vaccinated for COVID-19, but side effects, efficacy and length of testing remained concerns. Longer testing, increased efficacy and development in the United States were significantly associated with increased vaccine acceptance. Messages promoting COVID-19 vaccination should seek to alleviate the concerns of those who are already vaccine-hesitant. Messaging directed at the benefits of vaccination for the United States as a country would address the second predictive factor. Enough time should be taken to allay concerns about both short- and long-term side effects before a vaccine is released.

scholarly journals Reaping the Benefits of Long Term Relationship with Suppliers: An Evidence from the Saudi Chemical Industry

2016 ◽  
Vol 11 (8) ◽  
pp. 203
Author(s):  
Khalid M. Al-Shuaibi
Keyword(s):  
Supply Chain ◽  
Structural Equation Modeling ◽  
Financial Performance ◽  
Chemical Industry ◽  
Indirect Effect ◽  
Structural Equation ◽  
Equation Modeling ◽  
Supply Chain Performance

<p>Long term relationship with suppliers is broadly considered a vital contributor to supply chain performance by both practitioners and researchers. This paper investigates the role of long term relationship in strategic supplier partnership and financial performance (SSP-LR-P model). Specifically, it has observed the role of long-term supplier relationship as the driver of integration. Using structural Equation modeling (SEM) to analyze the data from 401 Saudi chemical and petrochemical firms, it is found that strategic supplier partnership has a significant direct and indirect effect on firms’ performance through the mediation of long term relationship.</p>

The Effects of Child and Adult Victimization on Psychological Distress: A Mediated Structural Equation Modeling Analysis

2019 ◽  
pp. 107780121988518
Author(s):  
Amanda Dishon-Brown ◽  
Seana Golder ◽  
Tanya Renn ◽  
TK Logan ◽  
George E. Higgins
Keyword(s):  
Structural Equation Modeling ◽  
Psychological Distress ◽  
Justice System ◽  
Structural Equation ◽  
Equation Modeling ◽  
Future Research ◽  
Mediation Model ◽  
Modeling Analysis ◽  
And Coping ◽  
The Relationship

Justice-involved women experience significantly higher rates of victimization and psychological distress, and these experiences place women at greater risk of initial and ongoing involvement in the criminal justice system. This research explored the relationship between victimization, the hypothesized mediators (social support and coping), and psychological distress among a sample of 406 victimized women on probation/parole. Results of the Structural Equation Modeling (SEM) were significant and indicated a partial mediation model (74%) with both direct and indirect effects. Based upon the results of this research, implications and future research are explored regarding gender-responsive practices for this population.

Impairment and Abuse of Elderly by Staff in Long-Term Care in Michigan: Evidence From Structural Equation Modeling

2010 ◽  
Vol 26 (1) ◽  
pp. 21-33 ◽  
Author(s):  
Tom Conner ◽  
Artem Prokhorov ◽  
Connie Page ◽  
Yu Fang ◽  
Yimin Xiao ◽  
...  
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Long Term Care ◽  
Equation Modeling ◽  
Term Care
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