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Impact of Covid-19 pandemic on female health care workers in Odisha (India)

MENTAL HEALTH AND SOCIAL INCLUSION 2021年第3期25卷 254-266页

作者： Pattayat, Shiba Shankar Patri, Prasanta Das, Silan Barik, Rajesh Cent Univ Punjab Dept Econ Studies Bathinda India Indian Inst Technol Indore Sch Humanities & Social Sci Indore India Ravenshaw Univ Dept Sociol Cuttack India

Purpose - The purpose of this paper is to investigate the impact of Covid-19 on female health workers (FHWs) in Odisha (India). Here, this study discusses the economic hardship of the FHWs during the pandemic time. Additionally, this study also identifies the various socio-economic factors responsible for the increasing mental stress level of FHWs in Odisha. Design/methodology/approach - The study is based on a telephonic survey method. Overall the study has collected 80 samples of FHWs from Odisha (India). Additionally, the study uses a logistic regression model to measure the impact of various socio-economic factors affecting the mental health status of FHWs during the pandemic time. Findings - The findings of this study depict that FHWs working as a Covid warrior during the pandemic time are facing severe economic vulnerability in their life. It is found that though the FHWs have worked tirelessly to save millions of human lives, but irregularity in salary payment and temporary jobs position have caused more mental distress to them. Moreover, the empirical findings have shown that factors such as joint family, social isolation, having an elderly member in their family, duty in Covid ward, staying in staff quarter and temporary job position are responsible for increasing the mental stress of FHWs in Odisha during the Covid-19 pandemic time. Originality/value - This study is a first attempt to explain the life struggle, economic vulnerability and mental stress of Indian (Odisha) FHWs working during the Covid-19 pandemic period to save millions of human lives.

关键词： Odisha logistic regression model Covid-19 Female health workers Telephonic survey

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Measurements and Determinants of Multidimensional Poverty: Evidence from Mountainous Areas of Southeast China

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JOURNAL OF SOCIAL SERVICE RESEARCH 2021年第5期47卷 743-761页

作者： Wang, Chengchao Zeng, Benxiang Luo, Dongshen Wang, Yuan Tian, Ying Chen, Shanshan He, Xiu Fujian Normal Univ Inst Geog Sci Fuzhou 350007 Peoples R China Fujian Normal Univ Coll Geog Sci Fuzhou Peoples R China Charles Darwin Univ Northern Inst Alice Springs NT Australia

Poverty is widely recognized as a multifaceted concept and has been extensively studied. However, less research has been conducted to explore multidimensional poverty in China. This article aims to investigate the measurements and determinants of multidimensional poverty in mountainous areas of Southeast China. The Alkire-Foster method and a logistic regression modeling technique are applied to examine multidimensional poverty and its determinants, respectively. Four dimensions (income, education, health, and living standards) and eight indicators are considered for the measurements of multidimensional poverty. By interviewing 198 rural households in Shouning County, Fujian Province of China, the article reveals about sixty percent of the sample households are found to experience multidimensional poverty, which is remarkably higher than previous studies. Deprivation in four dimensions is not homogeneous. The incidence of deprivation in education and health dimension are remarkably higher than dimensions of income and living standards. Results also reveal that health (34.27%) and education (27.45%) are the two most important contributors of multidimensional poverty, which jointly account for 61.72% of overall poverty. Living standards (23.25%) and income (15.03%) are the other two important contributors of multidimensional poverty. The important determinants of multidimensional poverty are the number of chronic patients, dependency ratio, elevation, and information accessibility. The results confirm that chronic disease or disability is the key determinant of poverty in rural areas of China. The study suggests that policy focus of poverty alleviation should be changed from income growth to health and education improvement. The provision and accessibility of health care services in rural areas need to be emphasized. Future research should focus on the heterogeneity of multidimensional poverty in China to expound the spatial difference and household difference in multi

关键词： Deprivation determinant logistic regression model multidimensional poverty relative poverty

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Identifying Socioeconomic Determinants of Households' Forest Dependence in the Rubi-Tele Hunting Domain, DR Congo: A logistic regression Analysis

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FORESTS 2022年第10期13卷 1706页

作者： Mendako, Richard K. Tian, Gang Matata, Patrick M. Northeast Forestry Univ Coll Econ & Management Harbin 150040 Peoples R China Univ Kisangani Fac Econ & Management Kisangani 2012 DEM REP CONGO

Rural households depend on forest resources for cash and subsistence needs. Thus, forests represent a valuable natural capital for the rural economy, particularly in developing countries. However, depending on various factors, there are dissimilarities in the rural livelihoods' reliance on forests. Therefore, this study attempted to determine and characterize the level of forest dependence and identify the demographic and socioeconomic factors influencing the households' dependence on forests in the Rubi-Tele Hunting Domain (RTHD)/Democratic Republic of the Congo (DR Congo). Demographic and socioeconomic data of forest dependents and other qualitative information were collected through structured household-level surveys, focus group discussions, and key informant interviews. Descriptive statistical analysis, Kruskal-Wallis Test, chi(2) test of independence, and binary logistic regression model were used for data analysis. The findings revealed that the forest dependence index varied from 0.01 to 1 (Mean = 0.46, SD = 0.30). The distribution of forest-dependent households by wealth status (income tercile) and level of forest dependence differs significantly. logistic regression revealed that household size and non-forest income were significant determinants of forest dependence and had the theoretically expected signs. The household size was in a positive association with forest dependence. Large families tended to depend more on forest resources. On the other hand, the non-forest income was inversely related to forest dependence, implying that forest-dependent households with non-forest income sources were less dependent on forest resources extraction. Other selected variables were not statistically significant while showing positive and negative associations with the reliance on forests. Achieving the balance between forest dependence and conservation requires promoting incentive policies to diversify livelihood opportunities and environmental education.

关键词： forest use forest income forest dependence rural livelihoods logistic regression model conservation Rubi-Tele Hunting Domain DR Congo

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Prediction of Accident Types and Severity of Highway Accidents Based on logistic regression

Prediction of Accident Types and Severity of Highway Acciden...

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2023世界交通运输大会(WTC2023)

作者： Chunjie Li Xuelin Gao Xiang Yang Qingyuan Wang Yanli Jiao Jianhua Hou Hebei Provincial Communications Planning Designand-Research:Institute-Co.Ltd School of Transportation Southeast-University Hebei Xiongan Rongwu Expressway limited company

In view of the frequent occurrence of highway accidents and the difficulty of traditional accident prediction methods to reflect the influence of dynamic characteristics of traffic flow on the accident type and severity,a sequential logistic regression-based method for predicting the accident type and severity of highway accidents is studied. The paper extracted traffic data from a domestic highway. After a series of variable screening,a three-stage regression model was established,with the first stage used to predict whether an accident occurred,the second stage to predict the type of accident,and the third stage to predict the severity of the accident. The results of the study showed that,low speed,large traffic flow rate,large daily traffic volume and many merging behaviors will lead to the increase of accident risk;low speed,large traffic volume and many diverging behaviors are prone to rear-end accidents;rear-end accidents occur at night,few vehicles on the roadway and mostly diverging vehicles may increase the severity of accidents. Through Hosmer-Lemeshow test,the models fit better. The AUC values of the model on the test samples were 0. 7177,0. 6924,and 0. 6905,respectively,through ten-fold cross-validation,which means that the models predict well.

关键词： Traffic safety Highway Prediction of accident types Prediction of accident severity logistic regression model

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Search Engine Consumer Journeys: Exploring and Segmenting Click-Through Behaviors

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CORNELL HOSPITALITY QUARTERLY 2021年第2期62卷 198-214页

作者： Cheng, Ming Anderson, Chris K. Suffolk Univ Sawyer Business Sch Mkt Boston MA 02108 USA Cornell SC Johnson Coll Business Ithaca NY USA

The consumer online search journey is viewed as one of the key determinants toward consumer purchase decisions. Using a unique disaggregated query-level data set collected from a leading Chinese search engine, we employ a latent-class, two-stage logistic regression model and identify the existence of three distinct consumer segments in the hospitality industry. Each group represents a unique stage of the consumer experience along a typical search journey, leading to a set of click-through behaviors. The consumer search and click-through insights garnered from this study provide hotel marketers with deeper understandings and actionable guidelines to the development of their search advertising campaign and (re)targeting strategies. The segmentation model and the empirical insights also demonstrate the possibility that a segment-specific ad assortment approach could improve advertising efficiency, enrich consumer's search experience, and eventually create a win-win situation that benefit all entities involved in the hospitality-related, search advertising domain.

关键词： consumer online search journey path-to-purchase latent class logistic regression model click-through rate hospitality industry

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Analysis of the Influencing Factors of Traffic Accidents Based on the logistics Method

Analysis of the Influencing Factors of Traffic Accidents Bas...

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International Conference on Smart Transportation and City Engineering

作者： Liu Bin Meng Yun-wei Wang Hong-hao Gong Wei-yong Luo Jian-qun Zunyi Highway Adm Bur Guizhou Prov Zunyi 563000 Guizhou Peoples R China Chongqing Jiaotong Univ Coll Traff & Transportat Chongqing 400074 Peoples R China China Merchants Chongqing Commun Technol Res & De Chongqing 400067 Peoples R China

ISBN: (数字)9781510649767

ISBN: (纸本)9781510649767;9781510649750

The influencing factors of the severity of highway traffic accidents are complex, and there is currently a lack of evaluation standards for all influencing factors, and there is a lack of comparative analysis among various factors. In order to study the causes of the severity of the consequences of highway accidents, this paper analyzes the traffic accident data of some sections of a highway, and uses the logistics method to divide the accident severity into two categories: general accidents and serious accidents, selecting weather, time, month and holidays as an independent variable. It is concluded that severe weather and holidays have a significant impact on the consequences of the accident, while the time factor and month factor have no significant impact. Based on professional knowledge, I put forward safety recommendations from traffic management and operation, traffic safety facility installation and traffic safety awareness.

关键词： expressway logistic regression model accident severity countermeasures

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Influence of meteorological variables on reference Evapotranspiration in the State of Alagoas, Brazil, based on multivariate analysis

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modelING EARTH SYSTEMS AND ENVIRONMENT 2021年第4期7卷 2215-2224页

作者： Correia Filho, Washington Luiz Felix dos Santos, Tatiana Vanessa de Barros Santiago, Dimas de Oliveira-Junior, Jose Francisco de Amorim, Ricardo Ferreira Carlos Fed Univ Alagoas UFAL Inst Atmospher Sci ICAT BR-57072260 Maceio Alagoas Brazil Fed Univ Campina Grande UFCG Postgrad Program Meteorol BR-58429140 Campina Grande Paraiba Brazil Fed Fluminense Univ UFF Postgrad Program Biosyst Engn PGEB BR-24210240 Niteroi RJ Brazil

Reference evapotranspiration (ETo) is an important agro-meteorological parameter used in several meteorological, climatological and hydrological studies. This study aimed to diagnose the effect of the meteorological variables and large-scale variability modes in ETo, based on logistic regression models. Monthly data from five conventional meteorological stations located on the State of Alagoas, Brazil, were used: agua Branca, Maceio, Pao de Acucar, Palmeira dos indios and Porto de Pedras, from 1960 to 2016 period. The variables used were as follows: temperatures (air-Tair, maximum-Tmax and minimum-Tmin), relative humidity (RH), sea level pressure, sunshine, wind speed (WS), rainfall, evaporation and also two climatic proxies: Oceanic Nino Index (ONI) and Tropical South Atlantic (TSA). ETo was estimated using the Penman-Monteith FAO method. The binary logistic regression procedure evaluated ETo values below the percentile 75 (ETo75 characterized as 1). Regarding the variables used the percentile 75 (except Tair and rainfall that used the percentile 30). The temperatures were the main variable for ETo75 events, except in agua Branca. The Tmax presented the highest odds ratio (OR) in Palmeira dos indios with value of 21.10 times on occurrence of ETo75. The wind is the most important variable in Pao de Acucar, due the heat capacity of the Sao Francisco River, with OR value of 3.72 times. The ONI did not integrate the models. Based on AIC and ROC curve values, Palmeira dos indios presented the best model with a value of 195.95 and 0.979, respectively.

关键词： Maximum Temperature Wind speed logistic regression model

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Whether a Criminal is Likely to Re-offend? A Statistical Analysis Using Crimes Data from Broward County Florida

Whether a Criminal is Likely to Re-offend? A Statistical Ana...

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作者： Xinyuan Wang Mingxin Liu Chengran Song Hongru Tan Faculty of Art and Science University of Toronto The Affiliated High School of Peking University College of Arts and Science Boston University Shenzhen college of international education

Security issues have always been a significant threat to the safety of citizens in every country, and many of these re-arrested criminals have negatively impacted social security. Therefore, predicting and studying the factors of a criminal's re-entry to prison will significantly help maintain social order and improve the civil society happiness index. This study, it will show what elements are predicted to influence a criminal's return to prison and what aspects will have a higher proportion and weight based on the collected data set. In the dataset, each re-admission inmate is categorized according to gender, age range, race, records, etc. Use the logistics model and OLS model to build a model to predict what factors most directly lead to a criminal being arrested and imprisoned again. Data research has proved that the "number of priors" is the factor that most affects the recidivism rate of criminals.

关键词： statistics prediction of recidivism rate ordinary least-squared model logistic regression model social inequality

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Does joint land ownership empower rural women socio-economically? Evidence from Eastern Nepal

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LAND USE POLICY 2024年 138卷

作者： Ghimire, Puspa Raj Devkota, Niranjan Maraseni, Tek Khanal, Ghanashyam Deuja, Jagat Khadka, Umesh Nepal Open Univ Fac Social Sci & Educ Manbhawan Lalitpur Nepal Tribhuvan Univ Kathmandu Model Coll Res Management Cell Kathmandu Nepal Univ Southern Queensland Toowoomba Qld 4350 Australia Auburn Univ Coll Forestry Wildlife & Environm Auburn AL 36849 USA Community Self Reliance Ctr CSRC Kathmandu Nepal Chinese Acad Sci Northwest Inst Ecoenvironm & Resources Lanzhou 730000 Peoples R China

Joint Land Ownership (JLO) is increasingly important for empowering women and improving their socioeconomic well-being. However, there is a lack of empirical research on this topic. Utilizing panel data from 2015 to 2020 and employing the Binary Logit model, this study examines the socioeconomic and empowerment status of rural women who have JLO in the Sunsari district of Nepal. Particularly, we used a quasi -experimental research design and pretest and posttest research models to assess the empowerment status of rural women before and after the implementation of JLO. The results indicate a significant increase in monthly savings (270%) among women who have a JLO, as well as improved access to credit (241%), income (87%), participation in cooperatives (69%), entrepreneurship (56%), decision -making (56%), occupation (22%), and overall improvement in their socioeconomic conditions (16%). The women's participation in the JLO program also has increased by 77% in rural areas and 23% in urban areas. Furthermore, logistic regression results show that Rural Women Empowerment (RWE), resulting from JLO, varies significantly across different ethnic groups (beta = -0.724, P < 0.01), land sizes (beta = 0.117, P < 0.01), household sizes (beta = 0.886, P < 0.01), household decision -making (beta = -1.814, P < 0.05), and occupations (beta = -0.868, P < 0.01). Our findings suggest that JLO significantly impacts rural women's empowerment, gender equality, and overall development, thereby assisting in the achievement of several UN Sustainable Development Goals. It emphasizes the need for policy interventions, legal reforms, and awareness -raising efforts to promote and support JLO to empower women and promote inclusive and sustainable rural development.

关键词： Join land ownership Rural women empowerment Pre-test post-test design logistic regression model Nepal

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Behavioral factors underlying energy consumption pattern: A cross-sectional study on industrial sector of Bangladesh

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HELIYON 2022年第11期8卷 e11523页

作者： Islam, Md. Shafiqul Rayhan, Md. Azaharul Islam Mojumder, Tanvir Hassan Univ Dhaka Dept Nucl Engn Dhaka 1000 Bangladesh Bangladesh Cent Bank Dhaka 1000 Bangladesh Univ Dhaka Energy Inst Dhaka 1000 Bangladesh

Energy consumption and wastage are increasing proportionally with the growth of the industrial sector of Bangladesh. This study explores the energy consumption behavior of industrial staff in the light of sociodemo-graphic and behavioral aspects using questionnaire sourced survey data. Based on the feedback of 827 employees from the 20 types of industries, behavioral constructs are formed using exploratory factor analysis and reliability analysis. In addition, a descriptive statistical analysis and a logistic regression model are used to identify the impact of these constructs as well as sociodemographic variables on staff's consumption behavior. Eight con-structs were selected based on the different behavioral theories and energy culture model. They include personal consumption behavior, technology adoption norms, training and supervision, openness to change, technological ignorance, energy self-efficacy, engagement, and responsibility. Analysis of the data reveals that awareness building program through training is notably neglected in the industries of Bangladesh. Also, the staff do not differ in consumption behavior in regards to gender, level of education, age-group, and educational background. Adopting new technologies, increasing awareness through training, and improving responsibility can significantly ameliorate consumption behavior, whereas ignorance of technology adoption makes consumption behavior worse. This work can be considered as baseline data on the energy conservation pattern in the industrial sector of Bangladesh. The findings recommend developing policies, regulations, and guides to give equal importance to both energy efficiency and conservation programs for exploiting maximum energy saving opportunities.

关键词： Energy conservation Industrial sector Questionnaire survey Behavioral construct logistic regression model

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