Explaining individual differences in infant visual sensory seeking

Individual differences in infants’ engagement with their environment manifest early in development and are noticed by parents. Three views have been advanced to explain differences in seeking novel stimulation. The optimal stimulation hypothesis suggests that individuals seek further stimulation when they are under‐responsive to current sensory input. The processing speed hypothesis proposes that those capable of processing information faster are driven to seek stimulation more frequently. The information prioritization hypothesis suggests the differences in stimulation seeking index variation in the prioritization of incoming relative to ongoing information processing. Ten‐month‐old infants saw 10 repetitions of a video clip and changes in frontal theta oscillatory amplitude were measured as an index of information processing speed. Stimulus‐locked P1 peak amplitude in response to checkerboards briefly overlaid on the video at random points during its presentation indexed processing of incoming stimulation. Parental report of higher visual seeking did not relate to reduced P1 peak amplitude or to a stronger decrease in frontal theta amplitude with repetition, thus not supporting either the optimal stimulation or the processing speed hypotheses. Higher visual seeking occurred in those infants whose P1 peak amplitude was greater than expected based on their theta amplitude. These findings indicate that visual sensory seeking in infancy is explained by a bias toward novel stimulation, thus supporting the information prioritization hypothesis.     

Learning from a lot: Empirical Bayes for high‐dimensional model‐based prediction

Empirical Bayes is a versatile approach to “learn from a lot” in two ways: first, from a large number of variables and, second, from a potentially large amount of prior information, for example, stored in public repositories. We review applications of a variety of empirical Bayes methods to several well‐known model‐based prediction methods, including penalized regression, linear discriminant analysis, and Bayesian models with sparse or dense priors. We discuss “formal” empirical Bayes methods that maximize the marginal likelihood but also more informal approaches based on other data summaries. We contrast empirical Bayes to cross‐validation and full Bayes and discuss hybrid approaches. To study the relation between the quality of an empirical Bayes estimator and p, the number of variables, we consider a simple empirical Bayes estimator in a linear model setting. We argue that empirical Bayes is particularly useful when the prior contains multiple parameters, which model a priori information on variables termed “co‐data”. In particular, we present two novel examples that allow for co‐data: first, a Bayesian spike‐and‐slab setting that facilitates inclusion of multiple co‐data sources and types and, second, a hybrid empirical Bayes–full Bayes ridge regression approach for estimation of the posterior predictive interval.     

Predatory purchasing practices in global apparel supply chains and the employment relations squeeze in the Indian garment export industry

While the literature has long acknowledged worker rights abuses in global apparel supply chains, less attention has been paid to the recent worsening of certain decent work deficits and its causes, which this article links to predatory purchasing practices arising from growing power asymmetries. These practices, illustrated here by the garment export industry in India, exert downward pressure on wages and incite non‐standard forms of employment and greater verbal and physical abuse as productivity demands rise. They can be addressed through pricing mechanisms that cover the cost of decent work, appropriate State regulation and worker involvement.     

Self-Stigma of seeking help and job burnout in mental health care providers: the comparative study of Lithuanian and the USA samples

This study aimed to explore the correlation between job burnout and self-stigma of seeking help among nonmedical mental health care providers (psychologists, social workers, and counselors) in two countries – Lithuania and the US. The study included 234 professionals (111 social workers and 123 psychologists) from Lithuania and 93 professionals (33 counselors, 23 social workers, and 37 psychologists) from the US on a voluntary basis (93% females, mean age – 39.81?years). They completed a self-reported questionnaire with the Self-Stigma of Seeking Help Scale (SSOSH) and the Maslach Burnout Inventory-General Survey (MBI-GS). The results revealed a statistically significant positive correlation between self-stigmatization and burnout in the Lithuanian sample, but only weak positive correlation between depersonalization and self-stigma of seeking help in the US sample. The relationship between the self-stigma of seeking help and burnout was stronger in the Lithuanian sample of professionals when compared to their colleagues in the US.     

Data‐Driven Supply Chain Orientation and Financial Performance: The Moderating Effect of Innovation‐Focused Complementary Assets

Drawing on the complementary assets framework, this study explores the moderating effect of innovation‐focused complementary assets (CA‐I) on the relationship between a data‐driven supply chain orientation (DDSCO) and firm financial performance. To test the moderating effect, survey data gathered from 329 manufacturing firms in China were analysed using a moderated regression analysis. The results indicate that DDSCO has a significant positive effect on financial performance, and that capabilities for product and process innovation function as complementary assets moderating the DDSCO–performance relationship. The findings suggest that innovation‐focused complementary assets are performance differentiators when paired with a DDSCO and explain why some firms obtain financial benefits from the development of a DDSCO while others do not. Specifically, competitive advantage from a DDSCO may not be realized unless CA‐I and potentially other complementary assets are harnessed, thus providing useful practical guidance to managers. Hence, the study provides empirical support for the complementary assets framework.     

Investigating the mechanism of marital mortality reduction: The transition to widowhood and quality of health care

While it is well known that the widowed suffer increased mortality risks, the mechanism of this survival disadvantage is still under investigation. In this article, we examine the quality of health care as a possible link between widowhood and mortality using a unique data set of 475,313 elderly couples who were followed up for up to nine years. We address whether the transition to widowhood affects the quality of care that individuals receive and explore the extent to which these changes mediate the elevated mortality hazard for the widowed. We analyze six established measures of quality of health care in a fixed-effect framework to account for unobserved heterogeneity. Caregiving and acute bereavement during the transition to widowhood appear to distract individuals from taking care of their own health care needs in the short run. However, being widowed does not have long-term detrimental effects on individuals’ ability to sustain contact with the formal medical system. Moreover, the short-run disruption does not mediate the widowhood effect on mortality. Nevertheless, long after spousal death, men suffer from a decline in the quality of informal care, coordination between formal and informal care, and the ability to advocate and communicate in formal medical settings. These findings illustrate women’s centrality in the household production of health and identify important points of intervention in optimizing men’s adjustment to widowhood.     

Mortality attributable to obesity among middle-aged adults in the united states

Obesity is considered a major cause of premature mortality and a potential threat to the longstanding secular decline in mortality in the United States. We measure relative and attributable risks associated with obesity among middle-aged adults using data from the Health and Retirement Study (1992–2004). Although class II/III obesity (BMI _ 35.0 kg/m2) increases mortality by 40% in females and 62% in males compared with normal BMI (BMI = 18.5-24.9), class I obesity (BMI = 30.0-34.9) and being overweight (BMI = 25.0-29.9) are not associated with excess mortality. With respect to attributable mortality, class II/III obesity (BMI _ 35.0) is responsible for approximately 4% of deaths among females and 3% of deaths among males. Obesity is often compared with cigarette smoking as a major source of avoidable mortality. Smoking-attributable mortality is much larger in this cohort: about 36% in females and 50% in males. Results are robust to confounding by preexisting diseases, multiple dimensions of socioeconomic status (SES), smoking, and other correlates. These findings challenge the viewpoint that obesity will stem the long-term secular decline in U.S. mortality.