Financial threat, hardship and distress predict depression, anxiety and stress among the unemployed youths: a Bangladeshi multi-cities study

Introduction: Unemployment has a contributory role in the development of mental health problems and in Bangladesh there is increasing unemployment, particularly among youth. Consequently, the present study investigated depression, anxiety, and stress among recent graduates in a multi-city study across the country. Methods: A cross-sectional study was conducted among 988 Bangladeshi graduate jobseekers in six major cities of the country between August to November 2019. The measures included socio-demographics and life-style factors, study and job-related information, Economic Hardship Questionnaire, Financial Threat Scale, Financial Well-Being Scale, and Depression Anxiety Stress Scale-21. Results: Depression, anxiety and stress rates among the present sample were 81.1% (n=801), 61.5% (n=608) and 64.8% (n=640) respectively. Factors related to gender, age, socio-economic conditions, educational background, lack of extra-curricular activities, and high screen activity were significant risk factors of depression, anxiety, and stress. Structural equation modeling indicated that (while controlling for age, daily time spent on sleep study, and social media use), financial threat was moderately positively related to depression, anxiety, and stress. Financial hardship was weakly positively associated with depression, anxiety, and stress, whereas financial wellbeing was weakly negatively associated with depression, anxiety, and stress. Limitations: Due to the nature of the present study (i.e., cross-sectional study) and sampling method (i.e., convenience sampling), determining causality between the variables is not possible. Conclusions: The present results emphasized the important detrimental role of financial troubles on young people's mental health by showing that financial problems among unemployed youth predict elevated psychiatric distress in both men and women

Some Findings about the Unemployed Highly Educated Persons in Pakistan

Two aspects of the problem of the unemployed educated persons are discussed in this essay. Firstly, the magnitude and incidence of the unemployment of such persons are examined. One point that becomes apparent from looking at the secondary data is that the bulk of the educated unemployed persons have been among those less than thirty years of age. Thus it appears that, at least in the past, most of the highly educated persons eventually got absorbed in the labour force. Secondly, in the light of the above, the important problem that comes to the fore is that of waiting. The results of an analysis of survey data, particularly on this dimension of the unemployment of the educated, have been reported here

Divisive and inegalitarian? Economic and social outcomes of public, private and faith-based education in Pakistan

Pakistan follows a diverse educational system consisting of three different tracks (public, private, and faith-based Madrasah education) with often conflicting objectives. As national cohesion has remained an elusive goal in Pakistan, it is important to know if there are systematic differences in the way graduates from different educational tracks access available opportunities. Using Pakistan Social and Living Standards Measurement (PSLM) survey data 2013-2014, we find that graduates from three tracks face different occupational choices and economic outcomes after their transition to the labor market and systematically differ with respect to the inter-generational transmission of educational and occupational opportunities. Additionally, we analyzed if graduates from the three educational types differ with respect to their socializing skills. Using the ‘sum-score’ approach to estimate the social exclusion, we found that graduates from private and Madrasah educational systems are the least and most socially excluded respectively

Ethylene glycol assisted low-temperature synthesis of boron carbide powder from borate citrate precursors

B4C powders were synthesized by carbothermal reduction of ethylene glycol (EG) added borate citrate precursors, and effects of EG additions (0–50 mol% based on citric acid) on the morphologies and yields of synthesized B4C powders were investigated. The conditions most suitable for the preparation of precursor were optimized and optimum temperature for precursor formation was 650 °C. EG additions facilitated low-temperature synthesis of B4C at 1350 °C, which was around 100–300 °C lower temperature compared to that without EG additions. The lowering of synthesis temperature was ascribed to the enlargement of interfacial area caused by superior homogeneity and dispersibility of precursors enabling the diffusion of reacting species facile. The 20% EG addition was optimal with free residual carbon lowered to 4%. For smaller EG additions, the polyhedral and rod-like particles of synthesized product co-existed. With higher EG additions, the morphology of synthesized product was transformed into needle and blade-like structure

A computer vision-based system for recognition and classification of Urdu sign language dataset

Human beings rely heavily on social communication as one of the major aspects of communication. Language is the most effective means of verbal and nonverbal communication and association. To bridge the communication gap between deaf people communities, and non-deaf people, sign language is widely used. According to the World Federation of the Deaf, there are about 70 million deaf people present around the globe and about 300 sign languages being used. Hence, the structural form of the hand gestures involving visual motions and signs is used as a communication system to help the deaf and speech-impaired community for daily interaction. The aim is to collect a dataset of Urdu sign language (USL) and test it through a machine learning classifier. The overview of the proposed system is divided into four main stages i.e., data collection, data acquisition, training model ad testing model. The USL dataset which is comprised of 1,560 images was created by photographing various hand positions using a camera. This work provides a strategy for automated identification of USL numbers based on a bag-of-words (BoW) paradigm. For classification purposes, support vector machine (SVM), Random Forest, and K-nearest neighbor (K-NN) are used with the BoW histogram bin frequencies as characteristics. The proposed technique outperforms others in number classification, attaining the accuracies of 88%, 90%, and 84% for the random forest, SVM, and K-NN respectively