Conceptualizing an Interdisciplinary Collective Impact Approach to Examine and Intervene in the Chronic Cycle of Homelessness.

Homelessness is a persistent problem in the United States in general and in Southern California especially. While progress has been made in reducing the number of people experiencing homelessness in the United States from 2007 (647,000) to 2019 (567,000), it remains an entrenched problem. The purpose of this paper is to outline a novel, interdisciplinary academic-practice partnership model to address homelessness. Where singular disciplinary approaches may fall short in substantially reducing homelessness at the community and population level, our model draws from a collective impact model which coordinates discipline-specific approaches through mutually reinforcing activities and shared metrics of progress and impact to foster synergy and sustainability of efforts. This paper describes the necessary capacity-building at the institution and community level for the model, the complementary strengths and contributions of each stakeholder discipline in the proposed model, and future goals for implementation to address homelessness in the Southern California region.

Twitter-based measures of neighborhood sentiment as predictors of residential population health.

Several studies have recently applied sentiment-based lexicons to Twitter to gauge local sentiment to understand health behaviors and outcomes for local areas. While this research has demonstrated the vast potential of this approach, lingering questions remain regarding the validity of Twitter mining and surveillance in local health research. First, how well does this approach predict health outcomes at very local scales, such as neighborhoods? Second, how robust are the findings garnered from sentiment signals when accounting for spatial effects? To evaluate these questions, we link 2,076,025 tweets from 66,219 distinct users in the city of San Diego over the period of 2014-12-06 to 2017-05-24 to the 500 Cities Project data and 2010-2014 American Community Survey data. We determine how well sentiment predicts self-rated mental health, sleep quality, and heart disease at a census tract level, controlling for neighborhood characteristics and spatial autocorrelation. We find that sentiment is related to some outcomes on its own, but these relationships are not present when controlling for other neighborhood factors. Evaluating our encoding strategy more closely, we discuss the limitations of existing measures of neighborhood sentiment, calling for more attention to how race/ethnicity and socio-economic status play into inferences drawn from such measures.