Implementing eScreening for suicide prevention in VA post-9/11 transition programs using a stepped-wedge, mixed-method, hybrid type-II implementation trial: a study protocol

Older adults using social support to improve self-care (OASIS): Adaptation, implementation and feasibility of peer support for older adults with T2D in appalachia: A feasibility study protocol

INTRODUCTION

The prevalence of type 2 diabetes (T2D) is 17% higher in rural dwellers compared to their urban counterparts, and it increases with age, with an estimated 25% of older adults (≥ 65 years) diagnosed. Appropriate self-care is necessary for optimal clinical outcomes. Overall, T2D self-care is consistently poor among the general population but is even worse in rural-dwellers and older adults. In rural Kentucky, up to 23% of adults in Appalachian communities have been diagnosed with T2D and, of those, 26.8% are older adults. To attain optimal clinical outcomes, social environmental factors, including social support, are vital when promoting T2D self-care. Specifically, peer support has shown to be efficacious in improving T2D self-care behaviors and clinical and psychosocial outcomes related to T2D; however, literature also suggests self-selected social support can be obstructive when engaging in healthful activities. Currently available evidence-based interventions (EBIs) using peer support have not been used to prioritize older adults, especially those living in rural communities.

METHOD

To address this gap, we conducted formative research with stakeholders, and collaboratively identified an acceptable and feasible peer support EBI-peer health coaching (PHC)-that has resulted in improved clinical and psychosocial T2D-related outcomes among participants who did not reside in rural communities nor were ≥65 years. The goal of the proposed study is to use a 2x2 factorial design to test the adapted PHC components and determine their preliminary effectiveness to promote self-care behaviors and improve glycemic control among older adults living in Appalachian Kentucky. Testing the PHC components of the peer support intervention will be instrumental in promoting care for older adults in Appalachia, as it will allow for a larger scale intervention, which if effective, could be disseminated to community partners in Appalachia.

TRIAL REGISTRATION

This study was registered at www.clinicaltrials.gov (NCT06003634) in August 2023.

Preventing Suicide in Health Systems: How Can Implementation Science Help?

Suicide prevention is an emotive, complex goal for clinicians and health systems. Effective interventions for suicidality do exist; however, many patients do not receive them because implementation efforts tend to be time-limited and unsystematic. Implementation science is the study of methods to promote the systematic uptake of research findings and other evidence-based practices into routine practice. This narrative review introduces implementation science to suicide researchers, with a special focus on healthcare settings. We outline prominent theories, methods, and measures, as well as examples of implementation research from suicidology. By embracing the principles of implementation science, suicidologists can help to close the gap between evidence-based practice and routine practice, thereby improving the delivery and uptake of suicide-related interventions and prevention programs.

Scaling and sustaining COVID-19 vaccination through meaningful community engagement and care coordination for underserved communities: hybrid type 3 effectiveness-implementation sequential multiple assignment randomized trial

BACKGROUND

COVID-19 inequities are abundant in low-income communities of color. Addressing COVID-19 vaccine hesitancy to promote equitable and sustained vaccination for underserved communities requires a multi-level, scalable, and sustainable approach. It is also essential that efforts acknowledge the broader healthcare needs of these communities including engagement in preventive services.

METHODS

This is a hybrid type 3 effectiveness-implementation study that will include a multi-level, longitudinal, mixed-methods data collection approach designed to assess the sustained impact of a co-created multicomponent strategy relying on bidirectional learning, shared decision-making, and expertise by all team members. The study capitalizes on a combination of implementation strategies including mHealth outreach with culturally appropriate messaging, care coordination to increase engagement in high priority preventive services, and the co-design of these strategies using community advisory boards led by Community Weavers. Community Weavers are individuals with lived experience as members of an underserved community serving as cultural brokers between communities, public health systems, and researchers to co-create community-driven, culturally sensitive public health solutions. The study will use an adaptive implementation approach operationalized in a sequential multiple assignment randomized trial design of 300 participants from three sites in a Federally Qualified Health Center in Southern California. This design will allow examining the impact of various implementation strategy components and deliver more intensive support to those who benefit from it most. The primary effectiveness outcomes are COVID-19 vaccine completion, engagement in preventive services, and vaccine confidence. The primary implementation outcomes are reach, adoption, implementation, and maintenance of the multicomponent strategy over a 12-month follow-up period. Mixed-effects logistic regression models will be used to examine program impacts and will be triangulated with qualitative data from participants and implementers.

DISCUSSION

This study capitalizes on community engagement, implementation science, health equity and communication, infectious disease, and public health perspectives to co-create a multicomponent strategy to promote the uptake of COVID-19 vaccination and preventive services for underserved communities in San Diego. The study design emphasizes broad engagement of our community and clinic partners leading to culturally sensitive and acceptable strategies to produce lasting and sustainable increases in vaccine equity and preventive services engagement.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT05841810 May 3, 2023.

Time–motion analysis of external facilitation for implementing the Collaborative Chronic Care Model in general mental health clinics: Use of an interval-based data collection approach

Background: Facilitation is an effective strategy to implement evidence-based practices, often involving external facilitators (EFs) bringing content expertise to implementation sites. Estimating time spent on multifaceted EF activities is complex. Furthermore, collecting continuous time–motion data for facilitation tasks is challenging. However, organizations need this information to allocate implementation resources to sites. Thus, our objectives were to conduct a time–motion analysis of external facilitation, and compare continuous versus noncontinuous approaches to collecting time–motion data. Methods: We analyzed EF time–motion data from six VA mental health clinics implementing the evidence-based Collaborative Chronic Care Model (CCM). We documented EF activities during pre-implementation (4–6 weeks) and implementation (12 months) phases. We collected continuous data during the pre-implementation phase, followed by data collection over a 2-week period (henceforth, “a two-week interval”) at each of three time points (beginning/middle/end) during the implementation phase. As a validity check, we assessed how closely interval data represented continuous data collected throughout implementation for two of the sites. Results: EFs spent 21.8 ± 4.5 h/site during pre-implementation off-site, then 27.5 ± 4.6 h/site site-visiting to initiate implementation. Based on the 2-week interval data, EFs spent 2.5 ± 0.8, 1.4 ± 0.6, and 1.2 ± 0.6 h/week toward the implementation’s beginning, middle, and end, respectively. Prevalent activities were preparation/planning, process monitoring, program adaptation, problem identification, and problem-solving. Across all activities, 73.6% of EF time involved email, phone, or video communication. For the two continuous data sites, computed weekly time averages toward the implementation’s beginning, middle, and end differed from the interval data’s averages by 1.0, 0.1, and 0.2 h, respectively. Activities inconsistently captured in the interval data included irregular assessment, stakeholder engagement, and network development. Conclusions: Time–motion analysis of CCM implementation showed initial higher-intensity EF involvement that tapered. The 2-week interval data collection approach, if accounting for its potential underestimation of irregular activities, may be promising/efficient for implementation studies collecting time–motion data.