Increasing Opportunities for Inner-City Youth: The Feasibility of an Economic Empowerment Model in East Harlem and the South Bronx, New York

Youth of color are disproportionately likely to grow-up in poor, disadvantaged neighborhoods characterized by high levels of psychosocial stressors and inadequate supportive resources. Poverty and racial minority status correlate with an increased risk of high-school dropout, teen pregnancy, substance abuse, and sexually transmitted infections (STIs). Given these trends, child welfare researchers are developing various interventions to increase the protective resources and social opportunities available to youth of color. This article reports results of a preliminary, qualitative study that investigated the feasibility and acceptability of an economic empowerment intervention in the South Bronx and East Harlem, New York. Using focus groups and brief questionnaires with youth and their parents/guardians (N = 24 dyads), we explored attitudes toward youth educational savings accounts, financial planning classes, and mentorship for inner-city youth. Findings indicate a strong interest in an economic empowerment intervention among adolescents and their caregivers in these communities. These findings have implications for the design of larger-scale research programs that aim to improve inner-city youth's socio-economic wellbeing using economic empowerment models.     

Detecting and removing outlier(s) in electromyographic gait-related patterns

In this paper, we propose a method for outlier detection and removal in electromyographic gait-related patterns (EMG-GRPs). The goal was to detect and remove EMG-GRPs that reduce the quality of gait data while preserving natural biological variations in EMG-GRPs. The proposed procedure consists of general statistical tests and is simple to use. The Friedman test with multiple comparisons was used to find particular EMG-GRPs that are extremely different from others. Next, outlying observations were calculated for each suspected stride waveform by applying the generalized extreme studentized deviate test. To complete the analysis, we applied different outlier criteria. The results suggest that an EMG-GRP is an outlier if it differs from at least 50% of the other stride waveforms and contains at least 20% of the outlying observations. The EMG signal remains a realistic representation of muscle activity and demonstrates step-by-step variability once the outliers, as defined here, are removed.