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                <text>Predictors of emotional and behavioral problems among Indian adolescents: A clinic-based study</text>
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                <text>Adolescent; Behavioural; Emotional; Parenting; Predictors</text>
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                <text>Background: Emotional and behavioral problems place a heavy burden on the adolescents and their families. Many factors are known to influence adolescent mental health. The current study was designed to determine the predictors of emotional and behavioral problems among Indian adolescents. Methods: The parents of adolescents in the age group of 10 to 18 (N = 81) were recruited from the National Institute of Mental Health and Neuro Sciences, Bangalore, Karnataka, India. Alabama Parenting Questionnaire (Parent version), Strength and Difficulty Questionnaire (Parent version) and, the Parent Interview Schedule (PIS) were used to assess the parental practices, emotional and behavioral problems and abnormal psychosocial environment of the adolescents. The data were analyzed with stepwise multiple linear and Quantile regression to find out significant predictors of emotional and behavioral problems among adolescents. Results: Adolescent's age, parental involvement, and paternal age are the significant predictors of emotional problems. Parental mental disorder or deviance, gender, and inconsistent discipline are the significant predictors for conduct problems. Poor monitoring and supervision, paternal and maternal age are statistically significant predictors of prosocial skills among the adolescents. Inadequate or distorted intrafamilial communication and parental mental disorder are the significant predictors of total difficulties among the adolescents. Conclusion: The study validates the role of abnormal psychosocial environments and negative parenting practices as risk factors for emotional and behavioral problems among the adolescents. A comprehensive analysis which covers all possible variables related to adolescent mental health is mandatory for the health professionals before planning the intervention.  2018 Elsevier B.V.</text>
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                <text>Aboobaker S.; Jangam K.V.; Sagar K.J.V.; Amaresha A.C.; Jose A.</text>
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                <text>Asian Journal of Psychiatry, Vol-39, pp. 104-109.</text>
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                <text>Elsevier B.V.</text>
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                <text>&lt;a href="https://doi.org/10.1016/j.ajp.2018.12.002" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1016/j.ajp.2018.12.002&lt;/a&gt;
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                <text>ISSN: 18762018; PubMed ID: 30599450</text>
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                <text>Aboobaker S., Department of Psychiatric Social Work, National Institute of Mental Health and Neuro Sciences (NIMHANS), Bengaluru, 560029, Karnataka, India; Jangam K.V., Department of Psychiatric Social Work, National Institute of Mental Health and Neuro Sciences (NIMHANS), Bengaluru, Karnataka, India; Sagar K.J.V., Department of Child and Adolescent Psychiatry National Institute of Mental Health and Neuro Sciences (NIMHANS), Bengaluru, 560029, Karnataka, India; Amaresha A.C., Department of Sociology and Social Work, CHRIST (Deemed to be University), Bengaluru, 560029, Karnataka, India; Jose A., Department of Biostatistics, National Institute of Mental Health and Neuro Sciences (NIMHANS), Bengaluru, 560029, Karnataka, India</text>
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            <name>Title</name>
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                <text>Predictors of Hypertension among Indian Women of Reproductive Age Group: An Analysis from NFHS-5 Data</text>
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              <elementText elementTextId="93424">
                <text>Health Status Disparities; Hypertension; Obesity; Rural Population; Socioeconomic Factors; Urban Population</text>
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                <text>Introduction: Hypertension among women not only augments the risk of cardiovascular diseases but also leads to antenatal and intra-natal complications. Materials and Methods: A subset of data collected during National Family Health Survey-5, comprising of 7,24,115 women, 1549 years of age was analysed to identify key predictors of hypertension, using Probit Regression Model (PRM) which was run separately for rural and urban women. Results: Overall prevalence of hypertension among women of reproductive age group was 11% (10.4% and 12% in rural and urban areas respectively). 5% and 13.41% of women were obese and 1.2% and 2.6% were diabetic in rural and urban areas respectively. Obese, uneducated, rich women and those on medications showed higher prevalence, while women consuming milk, eggs, chicken, fruits, and vegetables daily showed lower prevalence. On using PRM, significant predictors of hypertension were increasing age, rural residence, pregnancy, increasing weight, diabetes, illiteracy, access to medical insurance, and indulgence in alcohol and smoking. Conclusion: Findings from the study contribute to the body of evidence favouring multifactorial causation. Hypertension awareness should be promoted especially among rural residents, older women, with emphasis on intake of balanced diet with less consumption of sodium and increased intake of fruits and vegetables.  2023 National Journal of Community Medicine.</text>
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                <text>Yadav A.K.; Kumar T.; Zargar T.; Sahni B.</text>
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                <text>National Journal of Community Medicine, Vol-14, No. 10, pp. 643-649.</text>
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                <text>MedSci Publications</text>
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                <text>&lt;a href="https://doi.org/10.55489/njcm.141020233105" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.55489/njcm.141020233105&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85173941383&amp;amp;doi=10.55489%2Fnjcm.141020233105&amp;amp;partnerID=40&amp;amp;md5=c2c638c950f2d56dcbe6ac6c0a744562" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85173941383&amp;amp;doi=10.55489%2fnjcm.141020233105&amp;amp;partnerID=40&amp;amp;md5=c2c638c950f2d56dcbe6ac6c0a744562&lt;/a&gt;</text>
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                <text>All Open Access; Gold Open Access</text>
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                <text>ISSN: 9763325</text>
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                <text>Yadav A.K., Christ University, Bangalore, India; Kumar T., CD hospital, Jammu, India; Zargar T., GMC Jammu, Jammu, India; Sahni B., ASCOMS and Hospital, Jammu, India</text>
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                <text>Predictors of online buying behaviour</text>
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                <text>This study creates a framework by looking into various research on customer acceptance of new selfservice technologies and internet purchasing behaviour systems. According to this research, customers' attitudes towards online purchasing are initially influenced by the direct impacts of relevant characteristics of online shopping. These characteristics include functional, utilitarian characteristics and usefulness, emotional and hedonic characteristics. It looks at the technology acceptance theory (TAM) established by David in 1989 and the theory of reasoned action (TRA) to understand factors determining the attitudes of users towards online shopping for users using technology. It also provides conceptual models by using the brand image of the online platform, past experiences of buyers, information related to the product, convenience of the shoppers and trust of the customers towards online shopping.  2024, IGI Global. All rights reserved.</text>
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                <text>Khanna S.; Ghosh F.C.M.A.N.; Kumar S.; William R.M.</text>
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                <text>Enhancing and Predicting Digital Consumer Behavior with AI, pp. 268-283.</text>
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                <text>IGI Global</text>
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                <text>2024-01-01</text>
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                <text>&lt;a href="https://doi.org/10.4018/979-8-3693-4453-8.ch014" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.4018/979-8-3693-4453-8.ch014&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195739925&amp;amp;doi=10.4018%2F979-8-3693-4453-8.ch014&amp;amp;partnerID=40&amp;amp;md5=58c5d3afd1b3f68714ad2ed1bd72dbbe" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195739925&amp;amp;doi=10.4018%2f979-8-3693-4453-8.ch014&amp;amp;partnerID=40&amp;amp;md5=58c5d3afd1b3f68714ad2ed1bd72dbbe&lt;/a&gt;</text>
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                <text>ISBN: 979-836934454-5; 979-836934453-8</text>
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                <text>Khanna S., Christ University, India; Ghosh F.C.M.A.N., Christ University, India; Kumar S., Christ University, India; William R.M., Christ University, India</text>
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                <text>Predictors of Positive Psychological Capital: An Attempt Among the Teacher Communities in Rural Jharkhand, India</text>
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                <text>emotional intelligence; meaningful work; perceived organizational support; proactive personality; Psychological capital; servant leadership</text>
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                <text>Effective spatial planning in an urban center is the need of the hour especially for offering affordable and sustainable houses. separate sheet. Spatial planning may help in achieving Sustainable Development goals (SDG) (09) Industry, Innovation and Infrastructure, (11) Sustainable cities and communities, and (15) Life on Land. The prefabricated house model can be used as a strategy in achieving above mentioned SDGs. It is important to study the prefabricated housing market for a country like India, considering its growing population and the necessity of access to affordable and sustainable houses. The main objective of this study is to identify the determinant factors of prefabricated houses and its impact on preference among urban consumers. The study is quantitative in nature and adopts a survey method. SEM model is used to analyze the data. A structured questionnaire is developed based on the objectives of the study. The questionnaire majorly focused on the perception of Sustainability, Affordability, Durability, Barriers, Opportunities, and Quality.  The Electrochemical Society</text>
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                <text>Nagarjuna G., School of Business and Management, Christ (Deemed to be University), BGR Campus, Bangalore, 560076, India; Arjun B.S., School of Business and Management, Christ (Deemed to be University), BGR Campus, Bangalore, 560076, India; Shabarisha N., School of Business and Management, Christ (Deemed to be University), BGR Campus, Bangalore, 560076, India; Gowri Shankar R., School of Business and Management, Christ (Deemed to be University), BGR Campus, Bangalore, 560076, India</text>
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                <text>Sharma K., Department of CSE, Galgotias College of Engineering and Technology, Greater Noida, India; Gigras Y., Department of CSE and IT, The NorthCap University, Gurugram, India; Sharma V., Department of CSE, Galgotias College of Engineering and Technology, Greater Noida, India; Hemanth D.J., Department of ECE, Karunya University, Coimbatore, India; Poonia R.C., CHRIST (Deemed to be University), Bangalore, Karnataka, India</text>
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                <text>Online</text>
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            <name>Language</name>
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                <text>English</text>
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            <name>Type</name>
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                <text>Prakasha G.S., Christ University, India; Lapina M., North-Caucasus Federal University, Russian Federation; Balakrishnan D., Prince Mohammad Bin Fahd University, Saudi Arabia; Sajid M., Aligarh Muslim University, India</text>
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