Najat Elgeberi

Weight loss was a motivator for substance use for over 40% of participants.The majority of participants were concerned about weight gain in recovery.Weight-related substance use was associated with...

INTRODUCTIONThe compounding issue of homelessness and substance use disorder is one of the most challenging public health problems facing communities today. Traditional treatments often fail to address the complex needs of this population pushing practitioners towards multi-dimensional models. Communities could benefit from providers offering same-day access to integrated care and longer treatment durations. This study provides a holistic and nuanced understanding of one program’s impact, contributing valuable evidence to the field of addiction and homelessness services.METHODSThe study used a convergent mixed-methods design on a treatment sample of 615 individuals including a 6-month and 12-month follow up. Quantitative and qualitative data were collected and analyzed concurrently but separately. The findings were then triangulated during the interpretation phase to create a comprehensive picture of the program’s effectiveness allowing the quantitative data to provide a broad overview of outcomes, while the qualitative data offers deep, contextual insights into the processes that lead to those outcomes.RESULTSFor participants who remained engaged, the program led to statistically significant and sustained improvements in psychological wellbeing (p < .001), with large effect sizes across internal, relational, and functional domains. Housing stability dramatically increased from 68.9% at baseline to 93.3% at 12 months, and employment rose from 12.8% to 61.5%. Results revealed that program graduation and duration were the strongest predictors of these positive outcomes. Qualitative analysis of participant and staff interviews identified success factors: a low-barrier, “one-stop-shop” model that provides immediate access to comprehensive services, a structured and safe environment, and a therapeutic community built on peer support and dedicated staff.CONCLUSIONThe evaluation proved that the program is a highly effective intervention for unhoused individuals with substance use disorders who remain in treatment. While high attrition rates limit the generalizability of the quantitative findings, the study demonstrates the program's profound potential. The integration of quantitative and qualitative data reveals a powerful model of holistic, client-centered care, proving that with the right support, lasting recovery is possible for even the most vulnerable populations. The key challenge is not the program’s efficacy but retaining clients long enough to achieve these life-changing benefits.

Safe real-world navigation for autonomous vehicles (AVs) requires robust perception and decision-making, especially in complex, multi-agent scenarios. Existing AV datasets are limited by their inability to capture diverse V2X communication scenarios, lack of synchronized multi-sensor data, and insufficient coverage of critical edge cases in multi-vehicle interactions. This paper introduces VRDeepSafety, a novel and scalable VR simulation platform that overcomes these limitations by integrating Vehicle-to-Everything (V2X) communication, including realistic latency, packet loss, and signal prioritization, to enhance AV accident prediction and mitigation. VRDeepSafety generates comprehensive datasets featuring synchronized multi-vehicle interactions, coordinated V2X scenarios, and diverse sensor data, including visual, LiDAR, radar, and V2X streams. Evaluated with our novel deep-learning model, VRFormer, which uniquely fuses VR sensor data with V2X using a probabilistic Bayesian inference, as well as a hierarchical Kalman and particle filter structure, VRDeepSafety achieved an 85% accident prediction accuracy (APA) at a 2 s horizon, a 17% increase in 3D object detection precision (mAP), and a 0.3 s reduction in response time, outperforming a single-vehicle baseline. Furthermore, V2X integration increased APA by 15%. Extending the prediction horizon to 3–4 s reduced APA to 70%, highlighting the trade-off between prediction time and accuracy. The VRDeepSafety high-fidelity simulation and integrated V2X provide a valuable and rigorous tool for developing safer and more responsive AVs.