scholarly journals Stressors of Nursing Interns and Their Influencing Factors: A Cross-Sectional Study

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Xiaoqing Wang ◽  
Yu Han ◽  
Runyu Chai ◽  
Rong Chai
Keyword(s):  
Clinical Practice ◽  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Influencing Factors ◽  
Linear Regression Model ◽  
Protective Factor ◽  
Multiple Linear Regression Model ◽  
Medical Industry ◽  
Partial Regression Coefficient

Objective. It is still unknown whether the stress level and stressors in Chinese nursing interns are influenced by teacher-related factors. This research was carried out for better understanding of the stress in nursing interns and distribution of stressors during their clinical practice and targeted measures to unwind the stress of nursing interns. Methods. A questionnaire survey, titled Questionnaire on Stressors of Nursing Interns during Clinical Practice, was conducted on nursing interns at a 3A Grade Hospital in Shandong Province. Characteristics of the nursing interns and stressors of nursing interns were collected. A multiple-linear regression model was used to explore the influencing factors of nursing interns’ scores. Results. A total of 132 nursing interns were investigated in this study, and the overall stress scores were calculated. The stressors during the internship include the nature and content of the job, role orientation, workload, conflict between study and work, practice preparation, and interpersonal relationships. Gender, education level, instructor encouragement, and parents engaged in the medical industry were adjusted in the multiple-linear regression model as covariates. All of these factors had significant impacts on the scores of stressors ( P  < 0.05), with the partial regression coefficient values of 13.38, −10.52, −5.02, 3.4, −9.89, −14.77, and −15.83 for factors like female, undergraduates, graduate students, never obtained encouragement from teachers, obtained encouragement from teachers occasionally, obtained encouragement from teachers frequently, and parents engaged in the medical industry, respectively. Conclusion. The stressors of nursing interns are mostly work-wise, and teachers’ encouragement is an important protective factor for nursing interns to reduce stress. Therefore, clinical instructors should take targeted measures in teaching methods and work arrangements according to the needs of interns.

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.

scholarly journals Magnetization Transfer Ratio of Peripheral Nerve and Skeletal Muscle

2021 ◽  
Author(s):  
Olivia Fösleitner ◽  
Véronique Schwehr ◽  
Tim Godel ◽  
Fabian Preisner ◽  
Philipp Bäumer ◽  
...  
Keyword(s):  
Skeletal Muscle ◽  
Body Weight ◽  
Sciatic Nerve ◽  
Linear Regression ◽  
Peripheral Nerve ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Magnetization Transfer ◽  
Multiple Linear Regression Model

Abstract Purpose To assess the correlation of peripheral nerve and skeletal muscle magnetization transfer ratio (MTR) with demographic variables. Methods In this study 59 healthy adults evenly distributed across 6 decades (mean age 50.5 years ±17.1, 29 women) underwent magnetization transfer imaging and high-resolution T2-weighted imaging of the sciatic nerve at 3 T. Mean sciatic nerve MTR as well as MTR of biceps femoris and vastus lateralis muscles were calculated based on manual segmentation on six representative slices. Correlations of MTR with age, body height, body weight, and body mass index (BMI) were expressed by Pearson coefficients. Best predictors for nerve and muscle MTR were determined using a multiple linear regression model with forward variable selection and fivefold cross-validation. Results Sciatic nerve MTR showed significant negative correlations with age (r = −0.47, p < 0.001), BMI (r = −0.44, p < 0.001), and body weight (r = −0.36, p = 0.006) but not with body height (p = 0.55). The multiple linear regression model determined age and BMI as best predictors for nerve MTR (R2 = 0.40). The MTR values were different between nerve and muscle tissue (p < 0.0001), but similar between muscles. Muscle MTR was associated with BMI (r = −0.46, p < 0.001 and r = −0.40, p = 0.002) and body weight (r = −0.36, p = 0.005 and r = −0.28, p = 0.035). The BMI was selected as best predictor for mean muscle MTR in the multiple linear regression model (R2 = 0.26). Conclusion Peripheral nerve MTR decreases with higher age and BMI. Studies that assess peripheral nerve MTR should consider age and BMI effects. Skeletal muscle MTR is primarily associated with BMI but overall less dependent on demographic variables.

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