Kevin Knight

Older adults with complex health and social needs are frequently referred to community-based services, yet follow-through remains low. Motivational barriers and misalignment with personal values may undermine engagement. This qualitative descriptive pilot study explored barriers to referral uptake and the role of relational engagement within the Age-Friendly Integrative Mobile Services (AIMS) model, using thematic analysis of field notes from a pilot implementation. Seven adults aged ≥65 in Tarrant County, Texas received in-home assessments and referrals across five domains. Quantitative measures included Mini-Cog, TUG, PHQ-9, and the Older People’s Quality of Life scale. Field notes were thematically analyzed. Participants had substantial needs—43% screened positive for cognitive impairment, 67% had mobility limitations, and all reported depressive symptoms—yet uptake was limited. Barriers included mistrust, emotional fatigue, and preference for independence. Despite this, participants deeply valued the interpersonal experience, emphasizing the importance of being visited, heard, and treated with respect. Findings support integrating a Values Support Specialist to build trust and align care with personal values.

Despite growing investment in age-friendly models and social prescribing, many older adults remain disconnected from services, even when those services align with identified needs. Findings from the Age-Friendly Integrative Mobile Services (AIMS) pilot indicate that this engagement gap (motivational barriers, relational mistrust, and value misalignment) is as limiting as access or infrastructure. Motivational engagement - defined as aligning care with a person's values, readiness, and trust - emerged as a critical yet overlooked driver of uptake. Centering trust, values alignment, and relational support within implementation strategies may enhance service uptake, sustain engagement, and improve equity in aging-in-place outcomes by ensuring care is both accessible and meaningful.

Justice-involved individuals have many cooccurring socioeconomic and medical needs. Here, we characterize the needs of justice-involved individuals with substance use history at risk for or living with human immunodeficiency virus (HIV) in Texas (TX) and Connecticut (CT). All participants are part of a larger trial linking justice-involved people to community-based substance use and HIV services. Comprehensive baseline needs were assessed; frequencies of needs were quantified and compared using chi-square analysis. Participants ( N = 356) reported greater socioeconomic than medical needs in both TX ( n = 198) and CT ( n = 158). Housing, transportation, food, employment, and financial benefits were endorsed by more than 60% of participants. Health needs were emphasized in both states. Demand in TX was higher for health insurance (52.0% vs. 26.6%, p < .01) and identification/documentation (60.6% vs. 46.2%, p < .01), but not naloxone access (25.3% vs. 38.6%, p < .01). Substance use treatment/harm reduction and HIV preexposure prophylaxis were low priorities overall. The prioritization of socioeconomic over medical needs suggests a hierarchy of needs and resources. State-specific differences may reflect different sociopolitical environments and resources. Despite the high risks of living with or acquiring HIV and overdose, most participants reported low need for HIV and/or substance use services.

In an effort to address health risks for HIV among justice-involved individuals, linkage to community services for HIV and substance use disorder prevention and treatment is critical. Stakeholder feedback informing the development of interventions aimed at linking individuals to care is paramount to ensuring the success of the intervention. The current study examines focus group data collected as part of a 5-year NIDA-funded project and presents this data within an implementation science framework. Six focus groups were conducted across four communities in two states. A total of 19 individuals representing 15 agencies, including both corrections and community healthcare providers, participated in the focus groups. A deductive coding strategy was used to code the focus group transcripts using Atlas.ti 9 software. Audio-recorded interviews were transcribed and coded by trained qualitative researchers. The resulting 1,485 quotes were queried and analyzed using the EPIS inner and outer contexts implementation framework for reporting the findings. Inner context themes encompassing organizational characteristics, staffing processes, and leadership highlight the importance of intra-agency communication, as well as the benefit of hiring peer navigators with lived experience. Outer context themes of interest include funding, sociopolitical context, and interorganizational networks, pointing to service barriers related to funding for PrEP and HIV care, legislative regulations, and the importance of communication to connect people with justice involvement to healthcare and other services. Results support the use of both patient navigation and mobile health unit models to connect justice-involved individuals to healthcare. The themes that emerged during the focus groups helped inform the ACTION study protocol, and the focus group process bolstered the connection between the represented agencies. Ultimately, these focus groups provided valuable information about the communities participating in the study and provided key insights regarding study intervention implementation. ClinicalTrials.gov, NCT05286879. Registered 25 February 2022. https://clinicaltrials.gov/study/NCT05286879 .

The Texas Christian University Hub of the Justice Community Opioid Innovation Network Project includes a partnership with state criminal justice (CJ) agencies with the goal of impacting state- and local-level policies and practices that will lead to increased access to and retention in community-based substance use treatment services among parole clients. This article outlines how we successfully fostered policy changes and includes a five-step process for how researchers can bridge the gap between research and CJ agencies to implement similar changes. This process included the use of various strategies to support the justice partners in their efforts to implement changes within their organization.

Background: People with substance use disorders often differ in their decision-making styles. The present study addressed the impact of two decision-making styles (rational and dependent) on outcomes from a StaySafe tablet computer app intervention designed to improve decision-making around health risk behaviors and previously found to be effective for justice-involved people receiving treatment for a substance use disorder and under community supervision. Objectives: Participants were justice-involved residents in residential treatment. After completing a baseline survey, participants were randomly assigned to either complete the StaySafe app or to a standard procedure condition; and then asked to complete a post-intervention survey three months after baseline (this protocol has been registered with clinicaltrials.gov NCT02777086): 348 participants completed a baseline survey and 238 completed the post-test survey. Outcomes included measures of confidence and motivation around HIV knowledge and risks and getting tested. Multilevel analyses addressed the hypothesis that outcomes were related to decision-making style. Multiple imputation (MI) was used to address the effects of missing data. Results: StaySafe was more effective for those in the lower half of the decision-making dependent scale for HIV risks (HIV-Knowledge, Hepatitis testing, HIV Services testing, and Sex Risk, as well as motivation for treatment. The decision-making rational scale was less consistently related to HIV risk. Conclusions: The present study showed individuals with substance use disorders who differed in their decision-making styles reacted differently to the StaySafe intervention. Two scales, rational decision making, and dependent decision making are relevant to consider with respect to interventions targeting improving decision making among drug users.

We investigate why neural machine translation (NMT) systems assign high probability to empty translations. We find two explanations. First, label smoothing makes correct-length translations less confident, making it easier for the empty translation to finally outscore them. Second, NMT systems use the same, high-frequency EoS word to end all target sentences, regardless of length. This creates an implicit smoothing that increases zero-length translations. Using different EoS types in target sentences of different lengths exposes and eliminates this implicit smoothing.