The Role of the Government in Addressing Social Issues in India: The Perceptions of Indian Undergraduate and Graduate Social Work Students

Using a convenience sample of 318 undergraduate and graduate social work students (193 women and 125 men) from a public university in India, this study examined the differences between Indian social work students' perceptions toward the role of government in addressing social issues in India. Using Predictive Analytics Software (PASW) 18, several statistical procedures were conducted to analyze the relationship between social work students' demographic variables and their perceptions of the government's role in addressing social issues in India. The results indicated that older social work students were more likely to perceive that the government should ensure equal opportunities for citizens. Female social work students were more likely to perceive that the government should ensure housing for citizens. BSW students were more likely to perceive that the government should ensure employment of citizens. Macro social work students were more likely to perceive that the government should ensure equal opportunities, housing, food, employment, and health insurance for citizens. The findings of this study may help social work educational administrators and faculty in India to develop a curriculum that provides social work students with the opportunity to critically examine the complex relationship between age, gender, marital status, number of children, socioeconomic status, educational status, area of concentration, and the role of the government in addressing social issues. Doing so will prepare social work students and professionals to think and engage in a manner that will allow them to challenge and advocate for a change to the status quo.     

Evaluation of the exercise intervention with the "Back School" education program in a Brazilian company of cigars: a case study

The purpose of this study was to assess the benefits of an exercise program associated to the educational phase of the "Back School Program" in the quality of life of workers from a cigar factory. This is a case study conducted over four months with three activities a week, two times a day, with the third day devoted to educational activities, focusing on healthy lifestyle, correct posture, postural orientations, daily living and work activities, work motivation, and other issues.     

Buying a pig in a poke: An experimental study of unconditional veto power

We study an ultimatum experiment in which the responder does not know the offer when accepting or rejecting. Unconditional veto power leads to acceptances, although proposers are significantly greedier than in standard ultimatum games, and this is anticipated by responders. We also elicit responders’ willingness to pay for (un)conditional veto power. The bids reveal a large endowment effect.     

Fat in the Fire? Science,the News Media,and the “Obesity Epidemic”2

In recent years, the “obesity epidemic” has emerged as a putative public health crisis. This article examines the interconnected role of medical science and news reporting in shaping the way obesity is framed as a social problem. Drawing on a sample of scientific publications on weight and health, and press releases and news reporting on these publications, we compare and contrast social problem frames in medical science and news reporting. We find substantial overlap in science and news reporting, but the news media do dramatize more than the studies on which they are reporting and are more likely than the original science to highlight individual blame for weight. This is partly due to the news media’s tendency to report more heavily on the most alarmist and individual‐blaming scientific studies. We find some evidence that press releases also shape which articles receive media coverage and how they are framed.     

Applications of Bayesian analysis to proof‐of‐concept trial planning and decision making

Decision making is a critical component of a new drug development process. Based on results from an early clinical trial such as a proof of concept trial, the sponsor can decide whether to continue, stop, or defer the development of the drug. To simplify and harmonize the decision‐making process, decision criteria have been proposed in the literature. One of them is to exam the location of a confidence bar relative to the target value and lower reference value of the treatment effect. In this research, we modify an existing approach by moving some of the “stop” decision to “consider” decision so that the chance of directly terminating the development of a potentially valuable drug can be reduced. As Bayesian analysis has certain flexibilities and can borrow historical information through an inferential prior, we apply the Bayesian analysis to the trial planning and decision making. Via a design prior, we can also calculate the probabilities of various decision outcomes in relationship with the sample size and the other parameters to help the study design. An example and a series of computations are used to illustrate the applications, assess the operating characteristics, and compare the performances of different approaches.     

Machine Learning Methods as a Tool for Predicting Risk of Illness Applying Next‐Generation Sequencing Data

Next‐generation sequencing (NGS) data present an untapped potential to improve microbial risk assessment (MRA) through increased specificity and redefinition of the hazard. Most of the MRA models do not account for differences in survivability and virulence among strains. The potential of machine learning algorithms for predicting the risk/health burden at the population level while inputting large and complex NGS data was explored with Listeria monocytogenes as a case study. Listeria data consisted of a percentage similarity matrix from genome assemblies of 38 and 207 strains of clinical and food origin, respectively. Basic Local Alignment (BLAST) was used to align the assemblies against a database of 136 virulence and stress resistance genes. The outcome variable was frequency of illness, which is the percentage of reported cases associated with each strain. These frequency data were discretized into seven ordinal outcome categories and used for supervised machine learning and model selection from five ensemble algorithms. There was no significant difference in accuracy between the models, and support vector machine with linear kernel was chosen for further inference (accuracy of 89% [95% CI: 68%, 97%]). The virulence genes FAM002725, FAM002728, FAM002729, InlF, InlJ, Inlk, IisY, IisD, IisX, IisH, IisB, lmo2026, and FAM003296 were important predictors of higher frequency of illness. InlF was uniquely truncated in the sequence type 121 strains. Most important risk predictor genes occurred at highest prevalence among strains from ready‐to‐eat, dairy, and composite foods. We foresee that the findings and approaches described offer the potential for rethinking the current approaches in MRA.     

Discusion

Resumen

En esta investigación se estudia la validez de una versión castellana de la escala «Environmental Concern Scale» (ECS) llevada a cabo con 716 estudiantes. Las puntuaciones obtenidas por los sujetos proambientalistas y no proambientalistas resultan significativamente diferentes. Asímismo, se estudian las relaciones entre las actitudes hacia la preocupación por el medio ambiente, la energía nuclear y la ideología política, mostrándose una relación entre ellas de diferente intensidad. Mientras la energía nuclear está fuertemente vinculada a la ideología política, no sucede en tal medida entre ésta y las actitudes conservacionistas, ya que tanto los de derechas como los de izquierdas participan de la preocupación por el medio ambiente, aunque no en la misma medida.