Peer paradox: the tensions that peer relationships raise for vulnerable youth

The contribution peer relationships make to positive adolescent development is well recognized. Accordingly, peer problem measures typically assess youth with few age‐appropriate peers as having peer problems. Yet, youth facing high levels of personal and/or social adversity may reduce their association with antisocial peers as part of coping or risk mitigation strategies. While such strategies will result in higher scores on peer problem measures, they may also facilitate resilience and constitute a resource social workers can draw on in their work with youth. To test this proposition of peer adaptation as a risk mitigation strategy, mixed‐methods data relating to two groups of youth who were exposed to different levels of adversity were compared on a standardized peer problem measure and a range of risk measures. Qualitative interviews extended this data and explored vulnerable youth perceptions of social withdrawal as a coping strategy. Results from the survey and qualitative data indicated that a subgroup of youth facing high levels of adversity restricted association with antisocial peers to reduce their behavioural risks. However, without adequate support from adults in both formal support systems and youths' social ecologies to compensate for the loss of peer friendships, this strategy did not reduce behavioural risk in the medium term. The social withdrawal strategy also appeared to heighten mental health concerns for these youth. The implications of this finding for the development of policy and practice with vulnerable youth are discussed.     

The Methuselah Effect: The Pernicious Impact of Unreported Deaths on Old-Age Mortality Estimates

We examine inferences about old-age mortality that arise when researchers use survey data matched to death records. We show that even small rates of failure to match respondents can lead to substantial bias in the measurement of mortality rates at older ages. This type of measurement error is consequential for three strands in the demographic literature: (1) the deceleration in mortality rates at old ages; (2) the black-white mortality crossover; and (3) the relatively low rate of old-age mortality among Hispanics, often called the “Hispanic paradox.” Using the National Longitudinal Survey of Older Men matched to death records in both the U.S. Vital Statistics system and the Social Security Death Index, we demonstrate that even small rates of missing mortality matching plausibly lead to an appearance of mortality deceleration when none exists and can generate a spurious black-white mortality crossover. We confirm these findings using data from the National Health Interview Survey matched to the U.S. Vital Statistics system, a data set known as the “gold standard” (Cowper et al. 2002) for estimating age-specific mortality. Moreover, with these data, we show that the Hispanic paradox is also plausibly explained by a similar undercount.     

The Relation of Negative Career Thoughts to Depression and Hopelessness

Although some research literature focuses on the integration of mental health and career counseling, there has been little that examines both areas in relation to depression and hopelessness. This study investigated the relationship among dysfunctional career thinking, depression, and hopelessness in a sample of 139 undergraduate and graduate students seeking drop‐in or individual career counseling services at a university career center. The authors found that two aspects of dysfunctional career thinking, decision‐making confusion and commitment anxiety, accounted for a significant amount of variance in depression. Decision‐making confusion also accounted for a significant amount of variance in hopelessness. Implications for counseling practice include the need for more careful screening of career clients who present with high levels of anxiety and negative thinking. Future research could involve more diverse client populations, such as unemployed adults, and explore the use of additional screening measures to assess the intersection of career and mental health issues.     

Risk Analysis for U.S. Offshore Wind Farms: The Need for an Integrated Approach

Wind power is becoming an increasingly important part of the global energy portfolio, and there is growing interest in developing offshore wind farms in the United States to better utilize this resource. Wind farms have certain environmental benefits, notably near‐zero emissions of greenhouse gases, particulates, and other contaminants of concern. However, there are significant challenges ahead in achieving large‐scale integration of wind power in the United States, particularly offshore wind. Environmental impacts from wind farms are a concern, and these are subject to a number of on‐going studies focused on risks to the environment. However, once a wind farm is built, the farm itself will face a number of risks from a variety of hazards, and managing these risks is critical to the ultimate achievement of long‐term reductions in pollutant emissions from clean energy sources such as wind. No integrated framework currently exists for assessing risks to offshore wind farms in the United States, which poses a challenge for wind farm risk management. In this “Perspective”, we provide an overview of the risks faced by an offshore wind farm, argue that an integrated framework is needed, and give a preliminary starting point for such a framework to illustrate what it might look like. This is not a final framework; substantial work remains. Our intention here is to highlight the research need in this area in the hope of spurring additional research about the risks to wind farms to complement the substantial amount of on‐going research on the risks from wind farms.     

Extension of the Application of Conway‐Maxwell‐Poisson Models: Analyzing Traffic Crash Data Exhibiting Underdispersion

The objective of this article is to evaluate the performance of the COM‐Poisson GLM for analyzing crash data exhibiting underdispersion (when conditional on the mean). The COM‐Poisson distribution, originally developed in 1962, has recently been reintroduced by statisticians for analyzing count data subjected to either over‐ or underdispersion. Over the last year, the COM‐Poisson GLM has been evaluated in the context of crash data analysis and it has been shown that the model performs as well as the Poisson‐gamma model for crash data exhibiting overdispersion. To accomplish the objective of this study, several COM‐Poisson models were estimated using crash data collected at 162 railway‐highway crossings in South Korea between 1998 and 2002. This data set has been shown to exhibit underdispersion when models linking crash data to various explanatory variables are estimated. The modeling results were compared to those produced from the Poisson and gamma probability models documented in a previous published study. The results of this research show that the COM‐Poisson GLM can handle crash data when the modeling output shows signs of underdispersion. Finally, they also show that the model proposed in this study provides better statistical performance than the gamma probability and the traditional Poisson models, at least for this data set.