Mapping Our Way: New Formations in Technical Services: Ohio Valley Group of Technical Services Librarians Annual Conference

Abstract

The annual meeting of the Ohio Valley Group of Technical Services Librarians (OVGTSL) was held May 16–18, 2007, at the Holiday Inn-University Plaza in Bowling Green, Kentucky. One hundred twenty technical services librarians from Indiana, Ohio, Kentucky, and points beyond gathered for programming centered around the theme of “Mapping Our Way: New Formations in Technical Services.”     

Simple diagnostic tools for inverstigating linear trends in time series

This paper presents a simple diagnostic tool for time series. Based on a coefficient α that veries between 1 and 0, the tool measures the approximation of a time series to an arithmetic progression (i.e., a linear function of time). The proposed α is based on the ratio of the average squared second difference to the average squared first difference of the ginven series. As such, α reduces to the Von Neumann ratio η of the series of first differences, namely, α = 1-η/4. For an arithmetic progression α = 1, and deviations therefrom cause it to decrease. Unlike the correlation coefficient (between the entries and the indics), α is sensitive to local, or piecewise, linearity. Here α is evaluated for an assortment of simple time series models such as random walk, AR(1) and MA(1). Large-sample distribution yields a number of commonly used stochastic models including non-normal process. For most standard deterministic and stochastic models, α stabilizes as n approaches infinity, and provides a statistic that is capable of distinguishing between many different standard random and deterministic models. A further measure τ, which together with α distinguisches between random walks and deterministic trend plus i.i.d., is also suggested. Some examples based on empirical data are also studied.     

Predictors of substance use and family therapy outcome among physically and sexually abused runaway adolescents

There is a dearth of research that examines the impact of family systems therapy on problems among sexually and/or physically abused youth. Given this void, differential outcome and predictors of substance use change were evaluated for abused, as compared with nonabused, runaway adolescents who were randomly assigned to family therapy or treatment as usual. Abused adolescents reported lower family cohesion at baseline, although both abused and nonabused adolescents showed similar substance use reductions. Utilizing hierarchical linear modeling, we found that substance use changed with change in cohesion over time. These findings link change in family functioning to change in adolescent substance use, supporting fiamily systems theory. Findings suggest that a potent target of intervention involves focus on increasing positive communication interactions.     

Indian Family Exception Doctrine: still losing children despite the Indian Child Welfare Act

Since 1982, the Indian Family Exception Doctrine has been circumventing the Indian Child Welfare Act of 1978. Although not clearly defined, the doctrine has been pivotal in several American Indian child welfare cases in the United States. Over time, the doctrine continues to evolve and self-define. Several phrases have become part of the definition, such as Indian family and culture. This doctrine presents major concerns and implications in the field of child welfare.     

Longitudinal neural and behavioral trajectories of charity contributions across adolescence

This study examined the development of prosocial charity donations and neural activity in the ventral striatum when gaining rewards for self and for charity. Participants 10–22 years (95% European heritage) participated in three annual behavioral-fMRI waves (T1: n = 160, T2: n = 167, T3: n = 175). Behaviorally, donations to charity as measured with an economic Dictator Game increased with age. Perspective taking also increased with age. In contrast, self-gain and charity-gain enjoyment decreased with age. Ventral striatum activity was higher for rewards for self than for charity, but this difference decreased during adolescence. Latent growth curve models revealed that higher donations were associated with a smaller difference between ventral striatum activation for self and charity. These findings show longitudinal brain–donations associations in adolescence.     

Variability and change in adolescents' prosocial behavior across multiple time scales

We examined variability and change in adolescents' prosocial behaviors directed to peers and friends across four time scales: two-years, one-year, two-monthly, and daily. Data from three longitudinal datasets with a total of 569 adolescents (55.7% girl, M_age = 15.23, SD = 3.90) were included. The overall time-related stability of prosocial behavior across time scales was moderate to excellent. Variability did not differ between early (age 10–15) and late (age 16–21) adolescence, but late adolescence was associated with higher mean levels of prosociality. Finally, results indicated that prosocial behaviors measured over longer periods (i.e., two-years and one-year) were positively associated with cognitive processes (perspective taking), whereas prosocial behaviors measured over shorter periods (i.e., two-monthly) were positively associated with affective processes (empathy).     

Socioeconomic hardship,uncertainty about the future,and adolescent mental wellbeing over a year during the COVID-19 pandemic

Socioeconomic vulnerabilities put adolescents at risk for mental wellbeing issues, also in times of a pandemic. In the present longitudinal online survey study, we explored changes in mental wellbeing (i.e., mood and life satisfaction) during the COVID-19 pandemic. Second, we examined how socioeconomic hardship in online home schooling predicted adolescents’ mental wellbeing 1 year later. Third, we tested whether this relation was mediated by feelings of uncertainty about the future. Fourth, we tested whether this relation was moderated (in terms of a protective factor) by self-efficacy. In total, 177 Dutch-speaking adolescents aged 10–18 years (M_age = 15.64, SD_age = 1.72, 79% females) participated in all three 6-month separated waves (T1 = May 2020, T2 = November 2020, and T3 = May 2021). Mood results demonstrated that feelings of vigor decreased between T1 and T2, and feelings of tension and depression increased between T1, T2, and T3. Socioeconomic hardship in online home schooling in the early phase of the pandemic was negatively associated with both baseline mental wellbeing and 1 year later. Socioeconomic hardship at T1 predicted higher feelings of future uncertainty at T2, and higher levels of future uncertainty were associated with lower feelings of vigor, and higher feelings of tension and depression at T3. However, we found no evidence for mediation or moderation effects. In conclusion, the present findings illustrate the complexity of disentangling the adverse effects of socioeconomic hardship on adolescent mental wellbeing.     

Natural cubic splines for the analysis of Alzheimer's clinical trials

Mixed model repeated measures (MMRM) is the most common analysis approach used in clinical trials for Alzheimer's disease and other progressive diseases measured with continuous outcomes over time. The model treats time as a categorical variable, which allows an unconstrained estimate of the mean for each study visit in each randomized group. Categorizing time in this way can be problematic when assessments occur off-schedule, as including off-schedule visits can induce bias, and excluding them ignores valuable information and violates the intention to treat principle. This problem has been exacerbated by clinical trial visits which have been delayed due to the COVID19 pandemic. As an alternative to MMRM, we propose a constrained longitudinal data analysis with natural cubic splines that treats time as continuous and uses test version effects to model the mean over time. Compared to categorical-time models like MMRM and models that assume a proportional treatment effect, the spline model is shown to be more parsimonious and precise in real clinical trial datasets, and has better power and Type I error in a variety of simulation scenarios.