Mental Health Service Utilization among African American Emerging Adults

Mental illness affects 25-30% of US adults ages 18 years and older in a given year. Of those individuals, about 41% fail to utilize mental health services. Research indicates that being African American and between the ages of 18 and 29 are associated with decreased rates of service utilization. Yet, less is known about the factors related to mental health service utilization among a specific subset of these groups, namely, African American emerging adults. Using empirically-tested theoretical frameworks as its foundation, this dissertation study aimed to address this gap in literature by examining specific predisposing, enabling, and need factors: e.g., gender, educational attainment, employment, mental health insurance coverage, perceived emotional support, perceived racial discrimination, evaluated need, and perceived need) associated with mental health service utilization by recency of use: e.g., lifetime and past 12 month utilization) and provider types utilized: e.g., mental health versus non-mental health sector) among this group. Secondary analyses was conducted of a nationally representative sample of African American emerging adults: n=806), drawn from the National Survey of American Life. This study found that being female as well as having an evaluated need or a perceived need for services were significantly associated with greater odds of lifetime, past 12 month, and mental health sector providers. This study is a first step in future research to better understanding the factors associated with service utilization among this underserved population

A Systematic Review: The Effectiveness of Cultural Competence Trainings among Health Professions

Cultural competence trainings are cited as improving providers’ interactions with culturally diverse clients. Yet, little is known about the methodological rigor of such studies. To date, a systematic review of the effectiveness of cultural competence trainings by target population, intervention duration, and intervention intensity has not been conducted. An electronic systematic search was conducted to identify recent empirical evaluations of cultural competence trainings. Studies meeting the inclusion criteria were critically appraised using the Methodological Quality Rating Scale and the Outcome Attainment Index. Thirteen studies were included. Trainings targeting professionals were more rigorous than those targeting students. Accounting for methodological rigor, trainings targeting professionals, those longer in duration, and of higher intensity, achieved higher outcome attainment indices. Given the lack of rigor among these studies, it is difficult to assess their overall effectiveness. Considering the evergrowing diversification of this county, cultural competence trainings need to be both effective and rigorous

BUOCA: Budget-Optimized Crowd Worker Allocation

Due to concerns about human error in crowdsourcing, it is standard practice to collect labels for the same data point from multiple internet workers. We here show that the resulting budget can be used more effectively with a flexible worker assignment strategy that asks fewer workers to analyze easy-to-label data and more workers to analyze data that requires extra scrutiny. Our main contribution is to show how the allocations of the number of workers to a task can be computed optimally based on task features alone, without using worker profiles. Our target tasks are delineating cells in microscopy images and analyzing the sentiment toward the 2016 U.S. presidential candidates in tweets. We first propose an algorithm that computes budget-optimized crowd worker allocation (BUOCA). We next train a machine learning system (BUOCA-ML) that predicts an optimal number of crowd workers needed to maximize the accuracy of the labeling. We show that the computed allocation can yield large savings in the crowdsourcing budget (up to 49 percent points) while maintaining labeling accuracy. Finally, we envisage a human-machine system for performing budget-optimized data analysis at a scale beyond the feasibility of crowdsourcing.First author draf

BUOCA: Budget-Optimized Crowd Worker Allocation

Due to concerns about human error in crowdsourcing, it is standard practice to collect labels for the same data point from multiple internet workers. We here show that the resulting budget can be used more effectively with a flexible worker assignment strategy that asks fewer workers to analyze easy-to-label data and more workers to analyze data that requires extra scrutiny. Our main contribution is to show how the allocations of the number of workers to a task can be computed optimally based on task features alone, without using worker profiles. Our target tasks are delineating cells in microscopy images and analyzing the sentiment toward the 2016 U.S. presidential candidates in tweets. We first propose an algorithm that computes budget-optimized crowd worker allocation (BUOCA). We next train a machine learning system (BUOCA-ML) that predicts an optimal number of crowd workers needed to maximize the accuracy of the labeling. We show that the computed allocation can yield large savings in the crowdsourcing budget (up to 49 percent points) while maintaining labeling accuracy. Finally, we envisage a human-machine system for performing budget-optimized data analysis at a scale beyond the feasibility of crowdsourcing

Assessing the Effects of the New Cooperative Medical Scheme on Alleviating the Health Payment-Induced Poverty in Shaanxi Province, China

Copyright: © 2016 Yang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Background Disease has become one of the key causes of falling into poverty in rural China. The poor households are even more likely to suffer. The New Cooperative Medical Scheme (NCMS) has been implemented to provide rural residents financial protection against health risks. This study aims to assess the effect of the NCMS on alleviating health payment-induced poverty in the Shaanxi Province of China. Methods The data was drawn from the 5th National Health Service Survey of Shaanxi Province, conducted in 2013. In total, 41,037 individuals covered by NCMS were selected. Poverty headcount ratio (HCR), poverty gap and mean positive poverty gap were used for measuring the incidence, depth and intensity of poverty, respectively. The differences on poverty measures pre- and post- insurance reimbursement indicate the effectiveness of alleviating health payment-induced poverty under NCMS. Results For the general insured, 5.81% of households fell below the national poverty line owing to the health payment; this HCR dropped to 4.84% after insurance reimbursement. The poverty HCRs for the insured that had hospitalization in the past year dropped from 7.50% to 2.09% after reimbursement. With the NCMS compensation, the poverty gap declined from 42.90 Yuan to 34.49 Yuan (19.60% decreased) for the general insured and from 57.48 Yuan to 10.01 Yuan (82.59% decreased) for the hospital admission insured. The mean positive poverty gap declined 3.56% and 37.40% for two samples, respectively. Conclusion The NCMS could alleviate the health payment-induced poverty. The effectiveness of alleviating health payment-induced poverty is greater for hospital admission insured than for general insured, mainly because NCMS compensates for serious diseases. Our study suggests that a more comprehensive insurance benefit package design could further improve the effectiveness of poverty alleviation

Prevention of noise-induced hearing loss by calpain inhibitor MDL-28170 is associated with upregulation of PI3K/Akt survival signaling pathway

IntroductionNoise-induced calcium overload in sensory hair cells has been well documented as an early step in the pathogenesis of noise-induced hearing loss (NIHL). Alterations in cellular calcium homeostasis mediate a series of cellular events, including activation of calcium-dependent protein kinases and phosphatases. Using cell-membrane- and blood-brain-barrier-permeable calpain-1 (μ-calpain) and calpain-2 (m-calpain) inhibitor MDL-28170, we tested the involvement of calpains, a family of calcium-dependent cysteine proteases, and the potential of MDL-28170 in preventing NIHL.MethodsCBA/J mice at the age of 12 weeks were exposed to broadband noise with a frequency spectrum from 2–20 kHz for 2 h at 101 dB sound pressure level to induce permanent hearing loss as measured by auditory brainstem response and distortion product otoacoustic emissions. Morphological damage was assessed by quantification of remaining sensory hair cells and inner hair cell synapses 2 weeks after the exposure.ResultsMDL-28170 treatment by intraperitoneal injection significantly attenuated noise-induced functional deficits and cochlear pathologies. MDL-28170 treatment also prevented noise-induced cleavage of alpha-fodrin, a substrate for calpain-1. Furthermore, MDL-28170 treatment prevented reduction of PI3K/Akt signaling after exposure to noise and upregulated p85α and p-Akt (S473) in outer hair cells.DiscussionThese results indicate that noise-induced calpain activation negatively regulates PI3K/Akt downstream signaling, and that prevention of NIHL by treatment with MDL-28170 is associated with upregulation of PI3K/Akt survival signaling pathways

An unsupervised approach to discover media frames

Media framing refers to highlighting certain aspect of an issue in the news to promote a particular interpretation to the audience. Supervised learning has often been used to recognize frames in news articles, requiring a known pool of frames for a particular issue, which must be identified by communication researchers through thorough manual content analysis. In this work, we devise an unsupervised learning approach to discover the frames in news articles automatically. Given a set of news articles for a given issue, e.g., gun violence, our method first extracts frame elements from these articles using related Wikipedia articles and the Wikipedia category system. It then uses a community detection approach to identify frames from these frame elements. We discuss the effectiveness of our approach by comparing the frames it generates in an unsupervised manner to the domain-expert-derived frames for the issue of gun violence, for which a supervised learning model for frame recognition exists.Published versio