Livelihood Assets and Income Generating Activities: A Comparative Analysis in the Scheduled and Non-Scheduled Areas of Jharkhand

2021 ◽  
pp. 097370302110649
Author(s):  
Ashish Aman Sinha ◽  
Hari Charan Behera ◽  
Ajit Kumar Behura ◽  
Amiya Kumar Sahoo ◽  
Utpal Kumar De
Keyword(s):  
Latent Class ◽  
Multinomial Logistic Regression ◽  
Primary Source ◽  
Animal Husbandry ◽  
Livelihood Assets ◽  
Income Generating Activities ◽  
Daily Wage ◽  
Activity Cluster ◽  
Mlr Model

The main objective of the article is to identify different types of livelihood assets, income generating activities (IGAs) and choices of these activities by households across social groups in the Fifth and non-Fifth Scheduled areas of Jharkhand in eastern India. It is based on a primary survey of 785 households randomly selected across caste and Scheduled Tribe groups in Giridih and Latehar districts of Jharkhand. K-means clustering is applied for determination of latent class activity clusters and Multinomial Logistic Regression (MLR) model used for understanding the importance of livelihood assets in determining livelihood activity cluster (LC) for income generation. Further, discriminant analysis is applied to obtain probability of choice of individual households in determining livelihood generating activity. The analysis shows that forest-based activity remains a better livelihood support system in the Fifth Scheduled areas, which is less significant and further diminishing in the non-Fifth Scheduled areas. Rural households engaged in a diverse set of IGAs to obtain additional income to reduce risk and maintain a balanced consumption. Occupational transition is marked by the decline of agriculture and increasing reliance on daily-wage activities as the primary source of income. Other traditional livelihood activities such as animal husbandry and the collection of forest produce have less scope for income in the absence of institutional support.

scholarly journals Identifying classes of the pain, fatigue, and depression symptom cluster in long-term prostate cancer survivors—results from the multi-regional Prostate Cancer Survivorship Study in Switzerland (PROCAS)

2021 ◽  
Author(s):  
Salome Adam ◽  
Melissa S. Y. Thong ◽  
Eva Martin-Diener ◽  
Bertrand Camey ◽  
Céline Egger Hayoz ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Depressive Symptoms ◽  
Cancer Survivorship ◽  
Latent Class ◽  
Multinomial Logistic Regression ◽  
Symptom Cluster ◽  
Depression Symptom ◽  
Class 1 ◽  
Eortc Qlq

Abstract Purpose Aside from urological and sexual problems, long-term (≥5 years after initial diagnosis) prostate cancer (PC) survivors might suffer from pain, fatigue, and depression. These concurrent symptoms can form a cluster. In this study, we aimed to investigate classes of this symptom cluster in long-term PC survivors, to classify PC survivors accordingly, and to explore associations between classes of this cluster and health-related quality of life (HRQoL). Methods Six hundred fifty-three stage T1-T3N0M0 survivors were identified from the Prostate Cancer Survivorship in Switzerland (PROCAS) study. Fatigue was assessed with the EORTC QLQ-FA12, depressive symptoms with the MHI-5, and pain with the EORTC QLQ-C30 questionnaire. Latent class analysis was used to derive cluster classes. Factors associated with the derived classes were determined using multinomial logistic regression analysis. Results Three classes were identified: class 1 (61.4%) – “low pain, low physical and emotional fatigue, moderate depressive symptoms”; class 2 (15.1%) – “low physical fatigue and pain, moderate emotional fatigue, high depressive symptoms”; class 3 (23.5%) – high scores for all symptoms. Survivors in classes 2 and 3 were more likely to be physically inactive, report a history of depression or some other specific comorbidity, be treated with radiation therapy, and have worse HRQoL outcomes compared to class 1. Conclusion Three distinct classes of the pain, fatigue, and depression cluster were identified, which are associated with treatment, comorbidities, lifestyle factors, and HRQoL outcomes. Improving classification of PC survivors according to severity of multiple symptoms could assist in developing interventions tailored to survivors’ needs.

scholarly journals Physical Activity and Sedentary Activity Patterns Among Children and Adolescents: A Latent Class Analysis Approach

2011 ◽  
Vol 8 (4) ◽  
pp. 457-467 ◽  
Author(s):  
Carrie D. Patnode ◽  
Leslie A. Lytle ◽  
Darin J. Erickson ◽  
John R. Sirard ◽  
Daheia J. Barr-Anderson ◽  
...  
Keyword(s):  
Physical Activity ◽  
Latent Class Analysis ◽  
Children And Adolescents ◽  
Latent Class ◽  
Multinomial Logistic Regression ◽  
Activity Patterns ◽  
Moderate Activity ◽  
Class Analysis ◽  
Sedentary Behaviors ◽  
Sedentary Activity

Background:While much is known about the overall levels of physical activity and sedentary activity among youth, few studies have attempted to define clusters of such behaviors. The purpose of this study was to identify and describe unique classes of youth based on their participation in a variety of physical activity and sedentary behaviors.Methods:Latent class analysis was used to characterize segments of youth based on patterns of self-reported and accelerometer-measured participation in 12 behaviors. Children and adolescents (N = 720) from 6th-11th grade were included in the analysis. Differences in class membership were examined using multinomial logistic regression.Results:Three distinct classes emerged for boys and girls. Among boys, the 3 classes were characterized as “Active” (42.1%), “Sedentary” (24.9%), and “Low Media/Moderate Activity” (33.0%). For girls, classes were “Active” (18.7%), “Sedentary” (47.6%), and “Low Media/Functional Activity” (33.7%). Significant differences were found between the classes for a number of demographic indicators including the proportion in each class who were classified as overweight or obese.Conclusions:The behavioral profiles of the classes identified in this study can be used to suggest possible audience segments for intervention and to tailor strategies appropriately.

scholarly journals Decision Support Systems with AHP and SAW Method for Determination of Cattle with Superior Seeds

2016 ◽  
Vol 3 (2) ◽  
pp. 119-128
Author(s):  
Clarissa Amanda Josaputri ◽  
Endang Sugiharti ◽  
Riza Arifudin
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Body Condition Score ◽  
Animal Health ◽  
Animal Husbandry ◽  
Importance Value ◽  
Condition Score ◽  
Hierarchy Process

Department of Animal Husbandry and Fisheries of Semarang District is an institution in charge of livestock and animal health. Basically the Animal Husbandry Department has provided standardization for quality livestock cattle with superior seeds that usually can be judged or measured by various criteria.They are weight, age and value of BCS (Body Condition Score).They needed a system that could help the Department of Livestock and Fisheries of Semarang District in determining the electoral process cattle with superior seeds. In this research, the manufacture of Decision Support Systems in the determination cattle with superior seedsis using a combination of two methods is Analytical Hierarchy Process (AHP) and the Simple Addictive Weighting (SAW). In AHP will perform an importance value calculation criteria that will be paired up with an alternative to the SAW the next process is the sum of the weight from performance rating of all the attributes to each alternative, a ranking conducted to determine the result of cattle with superior seeds. Suggestions on this system, can be developed further by combining other methods to determine the recommendation that more effective.

scholarly journals Trajectories and Determinants of Physical Activity during COVID-19 Pandemic: A Population-Based Study of Middle-Aged and Elderly Individuals in The Netherlands

Nutrients ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 3832
Author(s):  
Amy Hofman ◽  
Marlou A. M. Limpens ◽  
Tosca O. E. de Crom ◽  
Mohammad Arfan Ikram ◽  
Annemarie I. Luik ◽  
...  
Keyword(s):  
Physical Activity ◽  
The Netherlands ◽  
Latent Class ◽  
Multinomial Logistic Regression ◽  
Public Health Problem ◽  
Population Based ◽  
World Health ◽  
Major Public Health Problem ◽  
Middle Aged ◽  
Health Organization

Physical inactivity is a major public health problem, and there are concerns this might have increased during the COVID-19 pandemic. We aimed to identify distinct trajectories of physical activity over a 6-week period after the first restrictive measures and to explore determinants of these trajectories in a population-based cohort of middle-aged and elderly in the Netherlands (n = 5777). We observed that at least 59% of participants did not meet the World Health Organization recommendations for physical activity. Using latent class trajectory analyses over three time points, we identified five distinct trajectories, including four steady trajectories at different levels (very low, low, medium and high) and one increasing trajectory. Using multinomial logistic regression analyses, we observed that, compared to the ‘steadily high’ trajectory, participants in the ‘steadily very low’ trajectory were more often older, lower educated, reporting poorer physical health, more depressive symptoms, consuming a less healthy diet, smoking, and lower alcohol use, and were less often retired. A similar pattern of determinants was seen for those in the increasing trajectory, albeit with smaller effect sizes. Concluding, we observed low levels of physical activity that generally remained during the pandemic. The determinants we described can help identify groups that require additional preventive interventions.

Application of latent class analysis in assessing the mental health of medical students during the COVID-19 Epidemic (Preprint)

2020 ◽  
Author(s):  
Fei Wang
Keyword(s):  
Mental Health ◽  
Medical Students ◽  
High Risk ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Risk Group ◽  
Multinomial Logistic Regression ◽  
High Risk Group ◽  
Class Analysis ◽  
Related Factors

BACKGROUND The novel coronavirus disease 2019 (COVID-19) is a global public health emergency that has caused worldwide concern. The mental health of medical students under the COVID-19 epidemic has attracted much attention. OBJECTIVE This study aims to identify subgroups of medical students based on mental health status and explore the influencing factors during the COVID-19 epidemic in China. METHODS A total of 29,663 medical students were recruited during the epidemic of COVID-19 in China. Latent class analysis of the mental health of medical students was performed using M-plus software to identify subtypes of medical students. The latent class subtypes were compared using the chi-square test. Multinomial logistic regression was used to examine associations between identified classes and related factors. RESULTS In this study, three distinct subgroups were identified, namely, the high-risk group, the low-risk group and the normal group. Therefore, medical students can be divided into three latent classes, and the number of students in each class is 4325, 9321 and 16,017. The multinomial logistic regression results showed that compared with the normal group, the factors influencing mental health in the high-risk group were insomnia, perceived stress, family psychiatric disorders, fear of being infected, drinking, individual psychiatric disorders, sex, educational level and knowledge of COVID-19, according to the intensity of influence from high to low. CONCLUSIONS Our findings suggested that latent class analysis can be used to categorize different medical students according to their mental health subgroup during the outbreak of COVID-19. The main factors influencing the high-risk group and low-risk group are basic demographic characteristics, disease history, COVID-19 related factors and behavioral lifestyle, among which insomnia and perceived stress have the greatest impact. School administrative departments could utilize more specific measures on the basis of different subgroups, and provide targeted measures.

DETERMINATION OF THE SENSITIVITY OF THE MICROBIOLOGICAL EXPRESS METHOD FOR DETECTING FLUOROQUINOLONE ANTIBIOTICS IN RAW MATERIALS OF MEAT USING THE TEST CULTURE BACILLUS STEAROTHERMOPHILUS

2021 ◽  
Vol 1 (3) ◽  
pp. 249-254
Author(s):  
G.M. Goryainova ◽  
◽  
L.V. Arsenyeva ◽  
E.A. Denisova ◽  
◽  
...  
Keyword(s):  
Bacillus Stearothermophilus ◽  
Raw Materials ◽  
Detection Limits ◽  
Animal Husbandry ◽  
Express Method ◽  
Antimicrobial Drugs ◽  
Test Culture ◽  
Fluoroquinolone Antibiotics ◽  
Wide Range

The article presents the results obtained during experiments to determine the sensitivity of the method according to GOST R 55481-2013 to antibiotics from the group of fluoroquinolones. GOST provides detection limits for such antibiotics as augmentin – 25.0 mcg/kg, benzylpenicillin – 4.0 mcg/kg, doxycycline – 10.0 mcg/ kg, cefazolin – 25.0 mcg/kg. Today, we are familiar with a wide range of different antimicrobial drugs used in animal husbandry, including an extensive group of drugs such as levofloxacin, norfloxacin, ofloxacin, marbocin, marfloxin, pefloxacin, ciprofloxacin, and others related to fluoroquinolones. We experimentally determined the sensitivity parameters for enrofloxacin – 24 mcg/l, levofloxacin – 26 mcg/l, ciprofloxacin – 24 mcg/l, marbofloxacin – 25 mcg/l.

The system of components dosing in compound feed

2021 ◽  
pp. 62-70
Author(s):  
S. Yu. Bulatov ◽  
V. N. Nechaev ◽  
A. G. Sergeev
Keyword(s):  
Animal Husbandry ◽  
Technological Parameters ◽  
Optimal Diet ◽  
Limit Values ◽  
Feed Production ◽  
Predominant Type ◽  
Dosing Accuracy ◽  
Selection Of ◽  
Poultry Farming

Feed production, feeding of animals and poultry is an integral part of animal husbandry and poultry farming. Proper feeding of animals and poultry, which implies the making of an optimal diet with the input of useful premixes and vitamins, can increase their productivity. In Russia the predominant type of feeding is complete feed, which includes compound feed. Regardless of the type of feed in the process of its production, it is necessary to observe the proportions of its components. Dispensers are used for dosing, which depending on the purpose, are divided into mass and volume. We have made an attempt to generalize, systematize and implement the accumulated experience in the form of a scheme that allows us to understand the principle of operation of modern systems for dosing feed components, in which augers are used as feeding mechanisms. The purpose of the research was to build a scheme for selecting parameters and develop a methodology for studying the dosage system of feed components based on it. The results of the analysis of intellectual property protection documents and scientifi c works in the fi eld of dosing have been used to make a scheme for selecting parameters of the feed components dosing system. The results of observations on the operation of the developed system under production conditions and design features have been also taken into account. As a result of the research developed the scheme of selection of the operating parameters of the dosing system, revealed its shortcomings in the form of lower dosing accuracy with the decrease in the mass of the weighed components and the long search settings when composing the new diet. The methods have been developed to address the identifi ed defi ciencies in the determination of limit values of technological parameters.

scholarly journals Associative stigma among mental health professionals in Singapore: a cross-sectional study

BMJ Open ◽  
2019 ◽  
Vol 9 (7) ◽  
pp. e028179 ◽  
Author(s):  
Louisa Picco ◽  
Sherilyn Chang ◽  
Edimansyah Abdin ◽  
Boon Yiang Chua ◽  
Qi Yuan ◽  
...  
Keyword(s):  
Mental Health ◽  
Job Satisfaction ◽  
Latent Class Analysis ◽  
Mental Health Professionals ◽  
Health Professionals ◽  
Online Survey ◽  
Latent Class ◽  
Multinomial Logistic Regression ◽  
Class Analysis ◽  
Associative Stigma

Objectives(1) Investigate and explore whether different classes of associative stigma (the process by which a person experiences stigmatisation as a result of an association with another stigmatised person) could be identified using latent class analysis; (2) determine the sociodemographic and employment-related correlates of associative stigma and (3) examine the relationship between associative stigma and job satisfaction, among mental health professionals.DesignCross-sectional online survey.ParticipantsDoctors, nurses and allied health staff, working in Singapore.MethodsStaff (n=462) completed an online survey, which comprised 11 associative stigma items and also captured sociodemographic and job satisfaction-related information. Latent class analysis was used to classify associative stigma on patterns of observed categorical variables. Multinomial logistic regression was used to examine associations between sociodemographic and employment-related factors and the different classes, while multiple linear regression analyses were used to examine the relationship between associative stigma and job satisfaction.ResultsThe latent class analysis revealed that items formed a three-class model where the classes were classified as ‘no/low associative stigma’, ‘moderate associative stigma’ and ‘high associative stigma’. 48.7%, 40.5% and 10.8% of the population comprised no/low, moderate and high associative stigma classes, respectively. Multinomial logistic regression showed that years of service and occupation were significantly associated with moderate associative stigma, while factors associated with high associative stigma were education, ethnicity and occupation. Multiple linear regression analyses revealed that high associative stigma was significantly associated with lower job satisfaction scores.ConclusionAssociative stigma was not uncommon among mental health professionals and was associated with sociodemographic factors and poorer job satisfaction. Associative stigma has received comparatively little attention from empirical researchers and continued efforts to address this understudied yet important construct in conjunction with future efforts to dispel misconceptions related to mental illnesses are needed.

scholarly journals Drivers of farm performance in Czech crop farms

2020 ◽  
Vol 66 (No. 7) ◽  
pp. 297-306
Author(s):  
Vladimír Kostlivý ◽  
Zuzana Fuksová ◽  
Tamara Rudinskaya
Keyword(s):  
Latent Class ◽  
Latent Class Model ◽  
Individual Characteristics ◽  
Class Model ◽  
Farm Accountancy Data Network ◽  
Class Definition ◽  
Structure Index ◽  
Farm Performance ◽  
Index Components

When analysing drivers affecting the farm performance, the presence of different technologies should be taken into account. We assume that the technology used by crop farms is not the same for all producers and therefore we use latent class model to identify technological classes at first. Class definition is based on multidimensional classification and determination of indices given by the values of individual components. The principal components analysis is applied to estimate significant and robust weights for the index components. FADN (Farm Accountancy Data Network) database, Czech crop farms data from 2005 to 2017 were used and three groups of technology classes of farms were identified with a determinant influence of the structure index and localisation. The other indices characterise sustainability, innovation, technology, diversification, and individual characteristics. Three distinct classes of crop farms were found, one major class and two minor classes. Family driven farms are usually smaller farms in terms of acreage. Highly sustainable crop farms are most likely located in lower altitudes and not in less-favoured areas. Innovative farms are also likely to be more productive. The results indicate that agricultural production farms with a more sustainable way of farming are most likely to be more productive.

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