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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., (© 2024. The Author(s).)

Background: Many previous studies of health care teamwork have taken place in clinical teams with high staffing ratios (i.e., high ratios of staff to patients)., Purpose: The aim of this study was to identify clinicians' viewpoints of foundational resources necessary to support good team functioning in the context of low staffing ratios., Methodology: We used administrative data, validated with local mental health chiefs, to identify mental health teams that had achieved high team functioning despite low staffing ratios in U.S. Department of Veterans Affairs medical centers. Guided by a recently developed model of team effectiveness, the Team Effectiveness Pyramid, we conducted qualitative interviews with 21 team members across three teams within two medical centers. Interview questions focused on the resources needed to support good team functioning despite low staffing ratios. We used directed content analysis to analyze results., Results: We found there were several domains of relevant resources: material, staffing, temporal, organizational, and psychological. These represent an expansion of the domains originally included in the Team Effectiveness Pyramid., Conclusions: Within the five domains, we identified key tensions to be addressed when forming teams, including the balances between providing care for new versus established patients, emphasizing shared caseloads within the team versus matching patients to clinicians based on individual expertise, and establishing reporting structures by clinical discipline versus team membership., Practice Implications: Establishing high-functioning health care teams in the context of low staffing ratios requires attention to key resource domains and fundamental trade-offs in how teams are structured., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

Background: Accompanying the rising rates of reported mental distress during the COVID-19 pandemic has been a reported increase in the use of digital technologies to manage health generally, and mental health more specifically., Objective: The objective of this study was to systematically examine whether there was a COVID-19 pandemic-related increase in the self-reported use of digital mental health tools and other technologies to manage mental health., Methods: We analyzed results from a survey of 5907 individuals in the United States using Amazon Mechanical Turk (MTurk); the survey was administered during 4 week-long periods in 2020 and survey respondents were from all 50 states and Washington DC. The first set of analyses employed two different logistic regression models to estimate the likelihood of having symptoms indicative of clinical depression and anxiety, respectively, as a function of the rate of COVID-19 cases per 10 people and survey time point. The second set employed seven different logistic regression models to estimate the likelihood of using seven different types of digital mental health tools and other technologies to manage one's mental health, as a function of symptoms indicative of clinical depression and anxiety, rate of COVID-19 cases per 10 people, and survey time point. These models also examined potential interactions between symptoms of clinical depression and anxiety, respectively, and rate of COVID-19 cases. All models controlled for respondent sociodemographic characteristics and state fixed effects., Results: Higher COVID-19 case rates were associated with a significantly greater likelihood of reporting symptoms of depression (odds ratio [OR] 2.06, 95% CI 1.27-3.35), but not anxiety (OR 1.21, 95% CI 0.77-1.88). Survey time point, a proxy for time, was associated with a greater likelihood of reporting clinically meaningful symptoms of depression and anxiety (OR 1.19, 95% CI 1.12-1.27 and OR 1.12, 95% CI 1.05-1.19, respectively). Reported symptoms of depression and anxiety were associated with a greater likelihood of using each type of technology. Higher COVID-19 case rates were associated with a significantly greater likelihood of using mental health forums, websites, or apps (OR 2.70, 95% CI 1.49-4.88), and other health forums, websites, or apps (OR 2.60, 95% CI 1.55-4.34). Time was associated with increased odds of reported use of mental health forums, websites, or apps (OR 1.20, 95% CI 1.11-1.30), phone-based or text-based crisis lines (OR 1.20, 95% CI 1.10-1.31), and online, computer, or console gaming/video gaming (OR 1.12, 95% CI 1.05-1.19). Interactions between COVID-19 case rate and mental health symptoms were not significantly associated with any of the technology types., Conclusions: Findings suggested increased use of digital mental health tools and other technologies over time during the early stages of the COVID-19 pandemic. As such, additional effort is urgently needed to consider the quality of these products, either by ensuring users have access to evidence-based and evidence-informed technologies and/or by providing them with the skills to make informed decisions around their potential efficacy., (©Dara H Sorkin, Emily A Janio, Elizabeth V Eikey, Margaret Schneider, Katelyn Davis, Stephen M Schueller, Nicole A Stadnick, Kai Zheng, Martha Neary, David Safani, Dana B Mukamel. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 16.04.2021.)

U.S. health care systems are tasked with alleviating the burden of mental health, but are frequently underprepared and lack workforce and resource capacity to deliver services to all in need. Digital mental health interventions (DMHIs) can increase access to evidence-based mental health care. However, DMHIs commonly do not fit into the day-to-day activities of the people who engage with them, resulting in a research-to-practice gap for DMHI implementation. For health care settings, differences between digital and traditional mental health services make alignment and integration challenging. Specialized attention is needed to improve the implementation of DMHIs in health care settings so that these services yield high uptake, engagement, and sustainment. The purpose of this article is to enhance efforts to integrate DMHIs in health care settings by proposing implementation strategies, selected and operationalized based on the discrete strategies established in the Expert Recommendations for Implementing Change project, that align to DMHI-specific barriers in these settings. Guidance is offered in how these strategies can be applied to DMHI implementation across four phases commonly distinguished in implementation science using the Exploration, Preparation, Implementation, Sustainment Framework. Next steps to advance research in this area and improve the research-to-practice gap for implementing DMHIs are recommended. Applying implementation strategies to DMHI implementation will enable psychologists to systematically evaluate this process, which can yield an enhanced understanding of the factors that facilitate implementation success and improve the translation of DMHIs from controlled trials to real-world settings. (PsycInfo Database Record (c) 2020 APA, all rights reserved).