Department of Informatics

This issue of the California Journal of Politics and Policy is produced in collaboration with the Kem C. Gardner Policy Institute at the David Eccles School of Business at the University of Utah.Drawing on the expertise of political scientists, economists, and practitioners from 13 west-ern states, the reports summarize each state’s budget for the 2017‒2018 fiscal year. These reports delve into how the states’ financial well-being affected legislation and just as importantly how legislation affected the states’ financial well-being.While most states seem to be financially sound, if not thriving, each report highlights possi-ble threats in the coming years, whether they be political, economic, or natural concerns. One theme across this year’s budget reports is how the 2016 election of President Donald Trump has affected legislation and fiscal health of the states. A second theme in the budget papers is the need to plan for the next recession.

This article provides three major contributions to the literature: we provide granular information on the development of student argumentative writing across secondary school; we replicate the MacArthur et al. model of Natural Language Processing (NLP) writing features that predict quality with a younger group of students; and we are able to examine the differences for students across language status. In our study, we sought to find the average levels of text length, cohesion, connectives, syntactic complexity, and word-level complexity in this sample across Grades 7-12 by sex, by English learner status, and for essays scoring above and below the median holistic score. Mean levels of variables by grade suggest a developmental progression with respect to text length, with the text length increasing with grade level, but the other variables in the model were fairly stable. Sex did not seem to affect the model in meaningful ways beyond the increased fluency of women writers. We saw text length and word level differences between initially designated and redesignated bilingual students compared to their English-only peers. Finally, we see that the model works better with our higher scoring essays and is less effective explaining the lower scoring essays.

Approximately 10 million people in the United States suffer from domestic violence annually, with 4 out of 10 cases affecting people of color. Traditional coloration guides remain the primary forensic strategy to evaluate bruise injuries, which are highly subjective and inaccurate for monitoring bruises. Additionally, this approach fails to consider bruise pigmentation in darker skin tones, and the results of this qualitative method vary by the medical professional conducting the inspection. There is a need for reliable, quantitative bruise information across all skin tones that can be utilized in both medicine and justice. DermaVision aims to address this need by designing a portable multi-spectral camera to quantitatively analyze bruises in diverse skin tones. By correlating the reflective spectra of a bruise with its age and healing progression, our camera will provide an accurate timeline for when bruises occur irrespective of patients’ skin color. This technology will assist forensics and medical professionals in improving their analyses and treatments and can provide valuable, admissible evidence in courts. For Validation purposes, a prototype imaging device has been developed to gather preliminary clinical data in collaboration with the University of California, Irvine Trauma Center. The team remains motivated to build a secure and reliable imaging tool that can serve the diverse population of domestic violence survivors. Faculty advisor Professor Elliot Botvinick

Peer support specialists (peers) who have the lived experience of, and are in recovery from, mental health challenges are increasingly being integrated into mental health care as a reimbursable service across the US. This study describes the ways peers were integrated into Help@Hand, a multi-site innovation project that engaged peers throughout efforts to develop and offer digital mental health interventions across counties/cities (sites) in California. Using a mixed methods design, we collected quantitative data via quarterly online surveys, and qualitative data via semi-annual semi-structured phone interviews with key informants from Help@Hand sites. Quantitative data were summarized as descriptive findings and qualitative data from interviews were analyzed using rapid qualitative analysis methods. In the final analytic phase, interview quotes were used to illustrate the complex realities underlying quantitative responses. 117 quarterly surveys and 46 semi-annual interviews were completed by key informants from 14 sites between September 2020 and January 2023. Peers were integrated across diverse activities for support and implementation of digital mental health interventions, including development of training and educational materials (78.6% of sites), community outreach (64.3%), technology testing (85.7%), technology piloting (90.9%), digital literacy training (71.4%), device distribution (63.6%), technical assistance (72.7%), and cross-site collaboration (66.7%). Peer-engaged activities shifted over time, reflecting project phases. Peer-provided digital literacy training and technology-related support were key ingredients for project implementations. This study indicates the wide range of ways peers can be integrated into digital mental health intervention implementations. Considering contextual readiness for peer integration may enhance their engagement into programmatic activities.

As AI systems proliferate, their greenhouse gas emissions are an increasingly important concern for human societies. In this article, we present a comparative analysis of the carbon emissions associated with AI systems (ChatGPT, BLOOM, DALL-E2, Midjourney) and human individuals performing equivalent writing and illustrating tasks. Our findings reveal that AI systems emit between 130 and 1500 times less CO2e per page of text generated compared to human writers, while AI illustration systems emit between 310 and 2900 times less CO2e per image than their human counterparts. Emissions analyses do not account for social impacts such as professional displacement, legality, and rebound effects. In addition, AI is not a substitute for all human tasks. Nevertheless, at present, the use of AI holds the potential to carry out several major activities at much lower emission levels than can humans.

As digital mental health interventions (DMHIs) proliferate, there is a growing need to understand the complexities of moving these tools from concept and design to service-ready products. We highlight five case studies from a center that specializes in the design and evaluation of digital mental health interventions to illustrate pragmatic approaches to the development of digital mental health interventions, and to make transparent some of the key decision points researchers encounter along the design-to-product pipeline. Case studies cover different key points in the design process and focus on partnership building, understanding the problem or opportunity, prototyping the product or service, and testing the product or service. We illustrate lessons learned and offer a series of questions researchers can use to navigate key decision points in the digital mental health intervention (DMHI) development process.