The full sample was used, rather than a split sample validation approach, due to sample size constraints for the Provider mhIST and remaining Consumer scales. Where feasible, we drew a stratified random sample of two-thirds of respondents from each study site for cross-site EFA when examining Consumer mhIST scales and used the remaining third for validation . Prior to cross-site analysis, we conducted EFA of each Consumer scale within each site, which informed methods used during cross-site analysis and are not presented here. We expected factors identified during EFA of the Provider mhIST domain scales to align with subscales defined during scale development (Table S2). We reviewed back-translated versions from these five studies, a back-translated version adapted for use in Pakistan by Usman et al. , and one Spanish-language translation by Marsch et al. . Adolescent participants did not complete Consumer mhIST as a part of the study.

Decision: Methods and tools to assess implementation of mental health policies and plans: A systematic review — R1/PR10

Open-text responses were reviewed for recurring themes or approaches to adaptation and validation. The names and contact information for the lead principal investigator for each study, as well as study descriptions, were abstracted into a sampling frame. Though we did not use a quantitative threshold (e.g., calculating an agreement statistic or a formal vote) to assess consensus, we did bring the expert panel together for a Zoom-based discussion of the summary of their questionnaire results, with a particular focus on areas of divergence. Implementation measure characteristics mapped to measure assessment approaches

• The literature on health care interventions and their effect on mental models as a specific concept is emerging.
• In low-resource settings where the treatment gap is large enough that the relationship between the supply of, and demand for, services has not reached equilibrium, the overwhelming demand for mental health care may lead to provider burnout, attrition, and ultimately a shortage of mental health providers.
• As the “interventions” of any implementation or sustainment endeavor, examining the comparative effectiveness and costs of implementation strategies is of great importance to clinical researchers as well as public and private health systems stakeholders.
• But most studies fail to specify a timeframe or are inconsistent in choice of a time point in the implementation process for measuring outcomes.
• Further research regarding this potential digital placebo effect is needed to isolate the active ingredients contributing to MH app effectiveness (Firth et al. 2017a).
• However, is the fact that there are a lot of people with mental illnesses on community supervision the issue that compels agencies to find an intervention?

The Good School Toolkit has been implemented in hundreds of primaryschools throughout the country and was recently adapted for secondary schools. With the increase in global enrollment rates, more children areattending primary and secondary school than ever before. The groupidentified a lack of services for the psychological sequelea of trauma and forcoping with the loss of loved ones.

Provider version

Negative recipient attitudes are one of the greatest barriers to MH app implementation; soliciting recipient opinions, for example through participatory or user-centered design approaches, should therefore be a critical component of app development (Vis et al. 2018). Alternatively, algorithm-based recommendations do not require human involvement but can provide users with suggestions tailored to their clinical presentation and usage patterns and may lead to increased app use (Cheung et al. 2018). The authors write, “most…digital health applications have not appropriately leveraged principles from theories of health behavior, which could be a major reason why recent evaluations suggest there has been little effect from them on health behaviors.” Patel et al. (2019) showed positive effects of a gamified app in increasing health behaviors; they drew from behavioral economics principles including precommitment to goals, loss aversion (e.g., not wanting to lose previously achieved gains), and the fresh start effect (e.g., giving users a clean slate after lapsing). As Bakker et al. (2016) review, gamification does not mean turning an app into a game, but rather to use “game-based mechanics, aesthetics, and game thinking to engage https://www.nationalacademies.org/read/26809/chapter/5 people, motivate action, promote learning, and solve problems” (e.g., SuperBetter, Roepke et al. 2015).

External Context

The mean app session length was 1 min, and the median session length was 17 s (Mohr et al. 2017b), emphasizing that app use patterns are categorically different than use of other modes of MH treatment. Intellicare has demonstrated high numbers of app launches and participant retention following trial completion (Mohr et al. 2019, 2017b). In another study of individuals with depression, the disorder-agnostic app SuperBetter, intended to increase resilience, optimism, and self-efficacy by completing challenges, was compared to a modified version with added CBT modules (Roepke et al. 2015). Apps should be simple, allowing users to engage with them in short bursts versus requiring long stretches of sustained attention (Zhang et al. 2019); indeed, MH apps designed for briefer interactions demonstrated higher engagement rates (Firth et al. 2017a). As the field matures, there have been increasing calls to incorporate design thinking strategies and user experience testing into MH app development (Lemon et al. 2020).