Independent Variables
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2022 ◽  
Vol 40 ◽  
Mateus Augusto Bim ◽  
André de Araújo Pinto ◽  
Gaia Salvador Claumann ◽  
Andreia Pelegrini

ABSTRACT Objective: To verify the prevalence of abdominal obesity with the waist-to-height ratio (WHtR) and associated factors in adolescents from a city in Southern Brazil. Methods: A total of 960 adolescents (494 boys) aged 15–18 years old participated in this study. The dependent variable was WHtR; independent variables were self-reported age, economic level, sexual maturation, physical activity level, screen time, and body fat. Data were analyzed using descriptive statistics and logistic regression. Results: It was observed that 36.7% of the adolescents presented high WHtR (50.2% in girls and 23.9% in boys). Regardless of sex, adolescents with high body fat were more likely of having high WHtR (boys: Odds Ratio [OR] 29.79; 95% confidence interval [95%CI] 16.87–52.62; girls: OR 19.43; 95%CI 10.51–35.94). In girls, high WHtR was associated with age (OR 1.83; 95%CI 1.17–2.87), and in boys, with economic level (OR 2.34; 95%CI 1.01–5.45). Conclusions: One in each three adolescents has abdominal obesity. Among adolescents with high body fat, girls aged 15–16 and boys with high-income are the groups most exposed to abdominal obesity.

2021 ◽  
Vol 7 (4) ◽  
pp. 571-586
Munevver Ilgun

<p style="text-align: justify;">Response times are one of the important sources that provide information about the performance of individuals during a test process. The main purpose of this study is to show that survival models can be used in educational data. Accordingly, data sets of items measuring literacy, numeracy and problem-solving skills of the countries participating in Round 3 of the Programme for the International Assessment of Adult Competencies were used. Accelerated failure time models have been analyzed for each country and domain.  As a result of the analysis of the models in which various covariates are included as independent variables, and response time for giving correct answers is included as a dependent variable, it was found the associations between the covariates and response time for giving correct answers were concluded to vary from one domain to another or from one country to another. The results obtained from the present study have provided the educational stakeholders and practitioners with valuable information.</p>

2021 ◽  
Vol 2 (1) ◽  
pp. 001-004
Abdul Rahman ◽  
Muhammad Yusuf

The right man in the right place and the right man behind the right job. The placement of employees becomes very important to improve employee performance including ASN in the government office of Mare Subdistrict, Bone Regency. The purpose of the study is to see the influence or role of employee placement factors on the performance of ASN employees. Apakh has a significant influence. The research was conducted on a number of ASNs in the government office of Mare District of Bone Regency. The method of data collection is done by saturated survey method (census). The method of data analysis is to use a hypothesis test, which is a t-test to see the influence of independent variables, namely placement (X) on dependent variables, namely ASN performance (Y). The results of the study obtained that the placement factor had a significant effect (real) on the performance of ASN in the District Office mare Bone Regency. This is evident from the results of the t test where the placement variable (X) has a significance value (Sig.=0.001<0.05) and is also shown from a t-calculated value (4.015) greater than the value of t-table (1,667).

2021 ◽  
Vol 21 (1) ◽  
Uche Shalom Obi ◽  
Chinyere Mbachu ◽  
Benjamin S. C. Uzochukwu

Abstract Background Conflicting schedules and geographic access limit prospects for mutually beneficial relationships between experts and early career professionals. A formal long-distance mentorship program could address these barriers and potentially bridge the gap of traditional face-to-face mentorship. This study was done to determine the feasibility of implementing a formal long-distance mentorship program amongst public health physicians of Nigeria. Method A mixed-method study comprising of in-depth interviews and surveys was used to collect information from members of the Association of Public Health Physicians in Nigeria. A total of 134 survey participants were recruited consecutively during an annual scientific meeting of the association. In-depth interviewees were purposively selected to ensure diversity in expertise, experience, and social stratifiers such as age. Quantitative data were analyzed using descriptive and inferential statistics, while qualitative data were analyzed using thematic content analysis. Results Public health physicians of Nigeria are willing to participate in a formal Long-Distance Mentorship Program, and four elements of feasibility were highlighted as necessary for implementing the program. Namely i) capacity to coordinate LDMP, ii) technical expertise and individual competence to provide mentorship, iii) financial capacity to implement and sustain LDMP, and iv) demand for mentorship by mentees. There is a consensus that the organizational structure of the National Postgraduate Medical College of Nigeria and West African College of Physicians provide an enabling environment to initiate a LDMP for public health physicians of Nigeria. The vast human resources with various expertise and the annual National conferences can be leveraged upon to champion and administer the program. However, there is a need for an administrative structure and technical expertise to enable proper coordination. More so, the need for demand creation and the financial requirement was considered gaps that need to be filled to be able to ensure feasibility. Bivariate analysis showed a significant relationship between the dependent variable (preferred role- mentor/mentee) and independent variables (age, year of graduation, and the number of years of practice), while the binary logistic regression model showed that physicians are more likely to participate as mentors with each unit increase in the number of years of practice. This further buttressed the need to commence the mentoring process as soon as trainees gain entrance into the program, as mentorship does not just prepare them for excellent public health practice, but also builds their capacity to mentor the younger and upcoming public health physicians. Conclusion There are enabling structures to incorporate a formal long-distance mentorship program for public health physicians in Nigeria, and physicians are willing to participate in such a program. However, the feasibility of establishing a successful and sustainable program will require robust coordination, technical expertise, demand creation, and financial commitment at both institutional and college levels.

2021 ◽  
Vol 3 (2) ◽  
pp. 91-100
Henny Medyawati ◽  
Muhamad Yunanto ◽  
Ega Hegarini

This study analyzes the influence of financial technology on the financial performance of banks listed on the Indonesia Stock Exchange (IDX) during the 2014-2020 period. Financial technology was measured by the number of Automated Teller Machine (ATM) transactions and internet and mobile banking, while bank profitability was measured by Return On Assets (ROA). Furthermore, this study used the panel data regression analysis, with the Automated Teller Machine (ATM) transactions as well as internet and mobile banking as the independent variables, and ROA as the dependent variable. Purposive sampling was used to select six banks as samples. The results showed the fixed effect as the most suitable model, where ROA is affected by the internet and mobile banking, while the TM technology has no effect.

2021 ◽  
Vol 13 (20) ◽  
pp. 11366
SeyedAli Ghahari ◽  
Cesar Queiroz ◽  
Samuel Labi ◽  
Sue McNeil

Any effort to combat corruption can benefit from an examination of past and projected worldwide trends. In this paper, we forecast the level of corruption in countries by integrating artificial neural network modeling and time series analysis. The data were obtained from 113 countries from 2007 to 2017. The study is carried out at two levels: (a) the global level, where all countries are considered as a monolithic group; and (b) the cluster level, where countries are placed into groups based on their development-related attributes. For each cluster, we use the findings from our previous study on the cluster analysis of global corruption using machine learning methods that identified the four most influential corruption factors, and we use those as independent variables. Then, using the identified influential factors, we forecast the level of corruption in each cluster using nonlinear autoregressive recurrent neural network models with exogenous inputs (NARX), an artificial neural network technique. The NARX models were developed for each cluster, with an objective function in terms of the Corruption Perceptions Index (CPI). For each model, the optimal neural network is determined by fine-tuning the hyperparameters. The analysis was repeated for all countries as a single group. The accuracy of the models is assessed by comparing the mean square errors (MSEs) of the time series models. The results suggest that the NARX artificial neural network technique yields reliable future values of CPI globally or for each cluster of countries. This can assist policymakers and organizations in assessing the expected efficacies of their current or future corruption control policies from a global perspective as well as for groups of countries.

Claudio Terranova ◽  
Giovanni Forza ◽  
Elena Beccegato ◽  
Angelo Ruggeri ◽  
Guido Viel ◽  

This study aimed to investigate the predictors of recidivism in first-time driving under the influence (DUI) offenders, analyzing variables derived from medico-legal and toxicological examinations. The research was structured as a comparative study for the period 2012–2019. DUI offenders with a blood alcohol concentration >0.5 were included in the study. The case group consisted of recidivist offenders, while the comparison group consisted of first-time offenders. Personal data, socioeconomics, and parameters linked to the DUI were compared between the two groups. Significance was determined by chi-square and Mann–Whitney tests. To prevent confounding effects, multivariate binary logistic regression analysis was performed. Our sample encompassed 1678 subjects (196 in the case group, 1482 in the comparison group). Gender, driving license category, education, and tobacco use resulted in significant differences between the groups. In a model including age at DUI, education, and smoking habit as independent variables, higher educational levels (high school, bachelor’s) and older age protected against recidivism, whereas smoking >20 cigarettes/day was an independent risk factor for recidivism. Recidivist offenders have specific characteristics indicating different therapeutic programs and carefulness in driving license regranting. A higher tobacco consumption in recidivists suggests that the use of this substance could influence the risk of DUI for reasons that will need to be explored.

2021 ◽  
Vol 2 (2) ◽  
pp. 212-225
Idris Saleh

This research aims to show the effect of capital adequacy ratio (CAR), financing to deposit ratio (FDR), non-performing financing (NPF), operating expenses on operating income (OEOI), and inflation partially and simultaneously on return on assets (ROA) at Sharia Commercial Bank in Indonesia. This type of research is a quantitative research using secondary data based on panel data. The research population consisted of 11 Islamic Commercial Banks in Indonesia using the purposive sampling technique so that 220 samples were obtained. The data analysis technique used is panel data regression method, classical assumption test, coefficient of determination, t-test, and f-test. The results show that CAR has a positive and significant effect on ROA, FDR has a negative and insignificant effect on ROA, NPF, and inflation has a positive effect and is not significant on ROA. At the same time, OEOI has a negative and significant effect on ROA. Simultaneously all independent variables have a significant effect on ROA.

Vaccines ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1179
Dimos D. Mitsikostas ◽  
Konstantina Aravantinou-Fatorou ◽  
Christina Deligianni ◽  
Evrydiki Kravvariti ◽  
Eleni Korompoki ◽  

Among healthcare workers (HCWs), SARS-CoV-2 vaccine hesitancy may be linked to a higher susceptibility to nocebo effects, i.e., adverse events (AEs) experienced after medical treatments due to negative expectations. To investigate this hypothesis a cross-sectional survey was performed with a self-completed questionnaire that included a tool (Q-No) for the identification of nocebo-prone individuals. A total of 1309 HCWs (67.2%women; 43.4% physicians; 28.4% nurses; 11·5% administrative staff; 16·6% other personnel) completed the questionnaires, among whom 237 (18.1%) had declined vaccination. Q-No scores were ≥15 in 325 participants (24.8%) suggesting nocebo-prone behavior. In a multivariate logistic regression model with Q-No score, age, gender, and occupation as independent variables, estimated odds ratios (ORs) of vaccination were 0.43 (i.e., less likely, p < 0.001) in participants with Q-No score ≥15 vs. Q-No score < 15, 0.58 in females vs. males (p = 0.013), and 4.7 (i.e., more likely) in physicians vs. other HCWs (p < 0.001), independent of age, which was not significantly associated with OR of vaccination. At least one adverse effect (AE) was reported by 67.5% of vaccinees, mostly local pain and flu-like symptoms. In a multivariate logistic regression model, with Q-No score, age, gender, and occupation as independent variables, estimated ORs of AE reporting were 2.0 in females vs. males (p < 0.001) and 1.47 in physicians vs. other HCWs (p = 0.017) independently of age and Q-No score, which were not significantly associated with OR of AE. These findings suggest that nocebo-prone behavior in HCWs is associated with SARS-CoV-2 vaccination hesitancy indicating a potential benefit of a campaign focused on nocebo-prone people.

Atmosphere ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1344
Peng Wang ◽  
Lyudmila Mihaylova ◽  
Rohit Chakraborty ◽  
Said Munir ◽  
Martin Mayfield ◽  

The monitoring and forecasting of particulate matter (e.g., PM2.5) and gaseous pollutants (e.g., NO, NO2, and SO2) is of significant importance, as they have adverse impacts on human health. However, model performance can easily degrade due to data noises, environmental and other factors. This paper proposes a general solution to analyse how the noise level of measurements and hyperparameters of a Gaussian process model affect the prediction accuracy and uncertainty, with a comparative case study of atmospheric pollutant concentrations prediction in Sheffield, UK, and Peshawar, Pakistan. The Neumann series is exploited to approximate the matrix inverse involved in the Gaussian process approach. This enables us to derive a theoretical relationship between any independent variable (e.g., measurement noise level, hyperparameters of Gaussian process methods), and the uncertainty and accuracy prediction. In addition, it helps us to discover insights on how these independent variables affect the algorithm evidence lower bound. The theoretical results are verified by applying a Gaussian processes approach and its sparse variants to air quality data forecasting.

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