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IntroductionAbout 20% of police calls involve a person experiencing a behavioral health crisis. To better address these community member’s needs and reduce risk of harm, jurisdictions are developing programs that shift calls for crises related to homelessness, addiction, and mental health to non-law enforcement alternatives. In responding to these calls, first responders working in these alternative response programs are exposed to a different set of moral dilemmas and pressures, increasing their risk of developing moral injury. MethodsThe Alternative Response Program Moral Injury and Burnout Model highlights causes of exposure to potentially morally injurious events and the interplay between moral distress, moral injury, and burnout among first responders working in alternative response programs. The conceptual model was developed through a multi-year participatory action research (PAR) study with members of a large urban alternative response program. The article draws on a highly diverse set of data sources that contributed to the development and refinement of the model, including focus group discussions, analysis of call logs, assessment of moral injury, and reflective conversations with members and leaders of the alternative response program. Through our collaboration, we identified contributory factors that can produce moral injury and opportunities for interventions to mitigate moral injury. We piloted several interventions collaboratively developed with team members and hypothesized to reduce moral distress and injury based on this model. ResultsOur model explains sources of moral dilemmas and how these can lead to moral distress, moral injury, and burnout. The model also points to intervention pathways to reduce or prevent these outcomes. We provide illustrations of how moral injury arises, using data from the program members, describe six pathways, and share examples for how moral injury may be mitigated. DiscussionDespite scarce research in first responders, moral injury and burnout are growing concerns in alternative response programs. Our goal was to amplify participants’ voices to highlight both their struggles as well as proposed interventions fostering resilience. Through close collaboration, we developed a moral injury model that resonates for these first responders and can guide a better understanding of issues and pathways for interventions among high-risk occupational groups. 

Not AvailableCost-effective localization methods for autonomous underwater vehicle navigation are key for ocean monitoring and data collection at high resolution in time and space. Algorithmic solutions suitable for real-time processing that handle nonlinear measurement models and different forms of measurement uncertainty will accelerate the development of field-ready technology. This article details a Bayesian estimation method for landmark-aided navigation using a side-scan sonar (SSS) sensor. The method bounds navigation filter error in the GPS-denied undersea environment and captures the highly nonlinear nature of slant range measurements with measurement origin uncertainty while remaining computationally tractable. Combining a novel measurement model with the chosen statistical framework facilitates the efficient use of SSS data and, in the future, could potentially be used in real-time. The proposed filter has two primary steps: a prediction step using an unscented transform and an update step utilizing particles. The update step performs probabilistic association of sonar detections with known landmarks. We evaluate algorithm performance and tractability using synthetic data and real data collected in field experiments. Field experiments were performed using two different marine robotic platforms with two different SSSs and at two different sites. Finally, we discuss the computational requirements of the proposed method and how it extends to real-time applications. 

Smartwatches have the potential to provide glanceable, always-available sound feedback to people who are deaf or hard of hearing (DHH). We present SoundWatch, a smartwatch-based deep learning application to sense, classify, and provide feedback about sounds occurring in the environment. To design SoundWatch, we first examined four low-resource sound classification models across four device architectures: watch-only, watch+phone, watch+phone+cloud, and watch+cloud. We found that the best model, VGG-lite, performed similar to the state of the art for nonportable devices although requiring substantially less memory (∼1/3 rd ) and that the watch+phone architecture provided the best balance among CPU, memory, network usage, and latency. Based on these results, we built and conducted a lab evaluation of our smartwatch app with eight DHH participants. We found support for our sound classification app but also uncovered concerns with misclassifications, latency, and privacy.