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.

BACKGROUND: Acute pain management is critical in postoperative care, especially in vulnerable patient populations that may be unable to self-report pain levels effectively. Current methods of pain assessment often rely on subjective patient reports or behavioral pain observation tools, which can lead to inconsistencies in pain management. Multimodal pain assessment, integrating physiological and behavioral data, presents an opportunity to create more objective and accurate pain measurement systems. However, most previous work has focused on healthy subjects in controlled environments, with limited attention to real-world postoperative pain scenarios. This gap necessitates the development of robust, multimodal approaches capable of addressing the unique challenges associated with assessing pain in clinical settings, where factors like motion artifacts, imbalanced label distribution, and sparse data further complicate pain monitoring. OBJECTIVE: This study aimed to develop and evaluate a multimodal machine learning-based framework for the objective assessment of pain in postoperative patients in real clinical settings using biosignals such as electrocardiogram, electromyogram, electrodermal activity, and respiration rate (RR) signals. METHODS: The iHurt study was conducted on 25 postoperative patients at the University of California, Irvine Medical Center. The study captured multimodal biosignals during light physical activities, with concurrent self-reported pain levels using the Numerical Rating Scale. Data preprocessing involved noise filtering, feature extraction, and combining handcrafted and automatic features through convolutional and long-short-term memory autoencoders. Machine learning classifiers, including support vector machine, random forest, adaptive boosting, and k-nearest neighbors, were trained using weak supervision and minority oversampling to handle sparse and imbalanced pain labels. Pain levels were categorized into baseline and 3 levels of pain intensity (1-3). RESULTS: The multimodal pain recognition models achieved an average balanced accuracy of over 80% across the different pain levels. RR models consistently outperformed other single modalities, particularly for lower pain intensities, while facial muscle activity (electromyogram) was most effective for distinguishing higher pain intensities. Although single-modality models, especially RR, generally provided higher performance compared to multimodal approaches, our multimodal framework still delivered results that surpassed most previous works in terms of overall accuracy. CONCLUSIONS: This study presents a novel, multimodal machine learning framework for objective pain recognition in postoperative patients. The results highlight the potential of integrating multiple biosignal modalities for more accurate pain assessment, with particular value in real-world clinical settings.

Recent progress in Generative AI (GenAI) impacts different software engineering (ES) tasks in software development cycle, e.g., from code generation to program repair, and presents a promising avenue for enhancing the productivity of development teams. GenAI based tools have the potential to change the way we develop software and have received attention from industry and academia. However, although some studies have been addressing the adoption of these tools in the software industry, little is known about what are developers' real experiences in a professional software development context, aside the hype. In this paper, we explore the use of GenAI tools by a large Brazilian media company that has teams developing software in-house. We observed practitioners for six weeks and used online surveys at different time points to understand their expectations, perceptions, and concerns about these tools in their daily work. In addition, we automatically collected quantitative data from the company's development systems, aiming at getting insights about how GenAI impacts the development process during the period. Our results provide insights into how practitioners perceive and utilize GenAI in their daily work in software development.

As GenAI becomes embedded in developer toolchains and practices, and routine code is increasingly generated, human creativity will be increasingly important for generating competitive advantage. This paper uses the McLuhan tetrad alongside scenarios of how GenAI may disrupt software development more broadly, to identify potential impacts GenAI may have on creativity within software development. The impacts are discussed along with a future research agenda comprising five connected themes that consider how individual capabilities, team capabilities, the product, unintended consequences, and society. can be affected.

UNLABELLED: Concerns regarding the potential negative impacts of digital technology use on youth mental health and well-being are high. However, most studies have several methodological limitations: relying on cross-sectional designs and retrospective reports, assessing technology use as an omnibus construct, and focusing on between- instead of within-person comparisons. This study addresses these limitations by prospectively following young adolescents (n = 388) over a 14-day ecological momentary assessment study to test whether adolescents digital technology use is linked with self-reported emotional dysregulation and self-esteem and whether these relationships are stronger for adolescent girls than boys. We found no evidence that adolescents experienced higher emotional dysregulation (b = - .02; p = .07) and lower self-esteem (b = .004; p = .32) than they normally do on days where they use more technology than they normally do (within-person). Adolescents with higher average daily technology use over the study period did not experience lower levels of self-esteem (between-person, b = - .02; p = .13). Adolescents with higher average daily technology use across the two-week period did report higher levels of emotional dysregulation (p = .01), albeit the between-person relation was small (b = .08). There was no evidence that gender moderated the associations, both between and within adolescents (bs = - .02-.13, p = .06 - .55). Our findings contribute to the growing counter-narrative that technology use does not have as large of an impact on adolescents mental health and well-being as the public is concerned about. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s42761-024-00282-w.

The recent proliferation of large language models (LLMs) has led to divergent narratives about their environmental impacts. Some studies highlight the substantial carbon footprint of training and using LLMs, while others argue that LLMs can lead to more sustainable alternatives to current practices. We reconcile these narratives by presenting a comparative assessment of the environmental impact of LLMs vs. human labor, examining their relative efficiency across energy consumption, carbon emissions, water usage, and cost. Our findings reveal that, while LLMs have substantial environmental impacts, their relative impacts can be dramatically lower than human labor in the U.S. for the same output, with human-to-LLM ratios ranging from 40 to 150 for a typical LLM (Llama-3-70B) and from 1200 to 4400 for a lightweight LLM (Gemma-2B-it). While the human-to-LLM ratios are smaller with regard to human labor in India, these ratios are still between 3.4 and 16 for a typical LLM and between 130 and 1100 for a lightweight LLM. Despite the potential benefit of switching from humans to LLMs, economic factors may cause widespread adoption to lead to a new combination of human and LLM-driven work, rather than a simple substitution. Moreover, the growing size of LLMs may substantially increase their energy consumption and lower the human-to-LLM ratios, highlighting the need for further research to ensure the sustainability and efficiency of LLMs.

Context: The adoption of remote work models and the global nature of software projects have significantly transformed collaboration and communication within the software development industry. Remote meetings have become a common means of collaboration for software development teams. Objective: This study seeks to enhance our understanding of remote meeting practices in software teams. It identifies the benefits of remote meetings, the problems associated with remote meetings, tools used to facilitate remote meetings and provides recommended good practices. The study employs a systematic literature review to assist remote teams in improving their meeting practices and identifying areas for future research. Methods: We conducted a systematic literature review that involved searching multiple databases and employing quantitative and qualitative analysis techniques on the identified set of studies to answer our research questions. Results: The search yielded 30 papers offering valuable insights into remote meeting practices in software teams. Remote meetings offer advantages over traditional in-person meetings such as increased effectiveness and ease of attendance. However, challenges exist such as technological issues, ineffective collaboration, and reduced team socialization. Identified good practices to mitigate the challenges include inserting breaks in longer meetings, catch-up time at the start of meeting, communicating goals in advance of the meeting, and pre-recording demos. Conclusion: The study explored remote meetings in software teams. We identified advantages that remote meetings have in comparison to in-person meetings, challenges to remote meetings, and good practices along with supportive tooling. While the practices help in promoting effective meetings, additional research is required to further improve remote meeting experiences. Researching topics such as investigating different types of meetings common to software development teams along with the potential for novel tools to better support meetings will help identify additional practices and tools that can benefit remote teams.

PURPOSE OF REVIEW: We evaluated the impact of digital mental health interventions (DMHIs) for college students. We organized findings using the RE-AIM framework to include reach, effectiveness, adoption, implementation, and maintenance. RECENT FINDINGS: We conducted a systematic literature review of recent findings from 2019-2024. Our search identified 2,701 articles, of which 95 met inclusion criteria. In the reach domain, student samples were overwhelmingly female and White. In the effectiveness domain, over 80% of DMHIs were effective or partially effective at reducing their primary outcome. In the adoption domain, studies reported modest uptake for DMHIs. In the implementation and maintenance domains, studies reported high adherence rates to DMHI content. While recruitment methods were commonly reported, adaptations and costs of implementation and maintenance were rarely reported. DMHIs for college students are effective for many psychological outcomes. Future work should address diversifying samples and considering implementation in a variety of college settings.

The Proof-of-Work algorithm that underlies Bitcoin, Ethereum w , 1 and many other cryptocurrencies is well known for its energy-intensive requirements. The Proof-of-Stake algorithm that underlies Ethereum and various other cryptocurrencies is less impactful environmentally, but it has a second, looming issue: the problem of wealth inequality. We have developed an alternative to Proof-of-Work and Proof-of-Stake, called Proof-by-Location, that has the potential to address both of these issues. This article describes Proof-by-Location and a financial platform called Xylem that is based on it. This platform seeks to distribute transaction fees to billions of cryptocurrency “Notaries” around the world (essentially, anyone with a smartphone), who work together to establish a distributed consensus about financial transactions. In this article, we demonstrate that this platform can scale to more than 3.9 trillion transactions per year (more than triple the number of digital payments per year currently occurring). We show a reduction of electricity usage per transaction of 99.9999914% compared to Bitcoin, 99.999905% compared to Ethereum w , 99.83% compared to Ethereum, and 95.9% compared to the Visa financial services company. We demonstrate that this platform would have a redistributive rather than consolidatory effect on wealth compared to any of these platforms, leading to a source of income for more than 1 billion people around the world, including more than 110 million in the bottom 10th to 20th percentile by income, with income for that group equivalent to 8.8 million full-time jobs. Finally, this currency provides a positive, non-compulsory mechanism for shaping human habitation patterns in ways that can slow global biodiversity loss and enable ecological restoration. Using Xylem as a global financial infrastructure could lead to significantly better social and environmental outcomes than existing financial platforms. 2

Cash transfer policies have been widely discussed as mechanisms to curb intergenerational transmission of socioeconomic disadvantage. In this paper, we take advantage of a large casino-funded family transfer program introduced in a Southeastern American Indian Tribe to generate difference-in-difference estimates of the link between childrens cash transfer exposure and third grade math and reading test scores of their offspring. Here we show greater math (0.25 standard deviation [SD], p =.0148, 95% Confidence Interval [CI]: 0.05, 0.45) and reading (0.28 SD, p = .0066, 95% CI: 0.08, 0.49) scores among American Indian students whose mother was exposed ten years longer than other American Indian students to the cash transfer during her childhood (or relative to the non-American Indian student referent group). Exploratory analyses find that a mothers decision to pursue higher education and delay fertility appears to explain some, but not all, of the relation between cash transfers and childrens test scores. In this rural population, large cash transfers have the potential to reduce intergenerational cycles of poverty-related educational outcomes.