Rescue Plan Research Articles

Recruitment and Retention of Direct Care Workers During the COVID-19 Pandemic: Challenges and Opportunities.

The COVID-19 pandemic exacerbated the challenges of home- and community-based service (HCBS) providers in recruiting and retaining direct care workers (DCWs). One of the largest and fastest-growing occupations in the U.S., DCWs are essential to the growing population of persons in need of long-term services who wish to remain in their homes. We surveyed all Connecticut Medicaid HCBS providers about their pandemic-era recruitment and retention challenges, strategies employed to overcome them, recommendations for regulatory and policy changes, and use of American Rescue Plan Act (ARPA) funding. Respondents (n = 447) noted the continuing key roles played by compensation and working conditions, emphasized unintended consequences of government unemployment policies, provided creative suggestions for professionalization of DCW work, and suggested best practices for consideration, including the success of word-of-mouth and employee referral bonuses in recruiting and the importance of organizational culture in retention.

Assessing the Implementation of ARPA Coronavirus State and Local Fiscal Recovery Funds: The View from City Officials

Abstract The 2021 American Rescue Plan Act created the Coronavirus State and Local Fiscal Recovery Funds (SLFRF), which provided $350 billion for state and local governments to aid with pandemic recovery efforts. This research examines the local implementation of SLFRF from the point of view of city officials. We assess the effectiveness of grant implementation on two dimensions: funding allocation (the ease or difficulty of selecting and prioritizing projects to be funded) and project management (the ability to complete projects on time and within budget). Our research focuses on the contributions of three general groups of factors on grant implementation performance: federal control and oversight, local capacities, and the role of local political actors. We conducted a national survey of cities that received SLFRF grants and gathered information from city officials responsible for administering the grant on different aspects of SLFRF implementation. Using ordinary least squares and ordered logit regressions to analyze the survey data, the empirical results uncover some consistent patterns. Specifically, local management discretion, perceived clarity of federal rules, a centralized local grant administration structure, and mayoral involvement are positively associated with grant implementation effectiveness.

Federal Priorities to Address Homelessness as a Community Health Problem

Homelessness is a major public health problem across communities in the United States. This commentary reviews required Learning Agendas of all 24 US federal agencies for items related to homelessness and housing instability covering the years 2022-2026. Across 7 federal agencies (Departments of Agriculture; Homeland Security; Labor; Health and Human Services; Housing and Urban Development; Treasury; Veterans Affairs) and 2 cross-government plans (American Rescue Plan Equity; Lesbian, Gay, Bisexual, Transgender, Queer, and Intersex Equity), there is a stated focus on homelessness and housing instability. These federal priorities seek to address homelessness and housing instability in families, Native American communities, unsheltered individuals, veterans, and sexual and gender minority individuals. Several agencies also have a stated focus on evaluating the impact of housing assistance and other supports provided during the COVID-19 pandemic. These findings reflect a national recognition and reframe of homelessness as an interdisciplinary, community health problem with opportunities for inter-agency collaboration and government support for primary and secondary prevention that have not been fully realized.

Knowledge Graphs to Accumulate and Convey Knowledge from Past Experiences in Search and Rescue Planning and Resource Allocation

ABSTRACT Planning and allocation of resources in search and rescue operations is critical and complex. Those tasks require consideration of several factors such as the nature of the emergency, location, people involved, and weather conditions. In this paper, we investigate the specific requirements for effective decision-making and planning in search and rescue operations in Norway. These requirements were determined using data gathered through expert interviews and analysis of mission reports. We propose a framework designed for search and rescue decision support, including its architecture and components. We also discuss the implementation of the service layer. We used information retrieval methods, i.e. BM25 and TF-IDF, for distance computation and compared their performance using k-means and hierarchical agglomerative clustering methods. The evaluation through silhouette score and Davies-Bouldin score shows that the hierarchical agglomerative clustering using BM25 performs well for the given dataset. The search and rescue mission reports published by the Norwegian Safety Authority were used as a dataset. In addition, the overall framework is evaluated by the domain experts using qualitative analysis. Finally, we discuss potential framework implementation challenges and provide suggestions for future research directions, in addition to presenting insights from the framework design process.

Rapid large-scale building damage level classification after earthquakes using deep learning with Lidar and satellite optical data

ABSTRACT In post-earthquake scenarios, the swift assessment of building damage levels is pivotal for efficient emergency response and recovery planning. Nevertheless, conventional in-situ damage evaluations consume time. Current satellite-based deep learning methods save time but often lack detail, usually classifying damage as either collapsed or intact. This two-level information is not enough for rescue or recovery planning. Light Detection and Ranging (Lidar)-based deep learning methods, which provide three-dimensional (3D) information, could address this issue of damage details. Therefore, this paper proposes a deep learning-based building damage level classification method using both Lidar and satellite data. The proposed method classifies damage into four levels, including no/minor damage, partially collapsed, totally collapsed, and story failure. The developed network builds upon RandLA-Net, incorporating surface normal vectors to enhance accuracy. A colourised Lidar dataset was created for the network. The network underscores the advantage of incorporating surface normal information. A framework is also proposed based on the damage level outcomes of the developed network, which aids in emergency response efforts. Consequently, this paper demonstrates the practical utility of deep learning networks in rapidly assessing detailed building damage levels after earthquakes. Its practical contribution is guiding decision-making during the critical phases of post-earthquake response and recovery.

Did the American Rescue Plan cause inflation? A synthetic control approach

This paper investigates the fiscal impact of the American Rescue Plan (ARP) on the post-pandemic surge in U.S. inflation. In the existing literature, the dominant strategy for identifying inflation determinants is through structural or reduced-form equation modeling; however, a definitive consensus on the causes of the U.S. inflationary episode remains elusive due to a lack of data-driven counterfactual analysis. To address this gap, we uniquely apply a synthetic control approach to estimate the causal effect of the ARP on U.S. inflation. The findings reveal that the ARP excessively stimulates aggregate demand via large-scale unfunded transfers, causing U.S. inflation to deviate upward from its counterfactual path. Moreover, this policy intervention significantly elevates inflation expectations and diminishes consumer confidence, thereby activating the fiscal limit mechanism: unfunded fiscal shocks, combined with waning fiscal credibility, lead private agents to anticipate that national debt will be inflated away, resulting in large and persistent inflation.

MmECare: Enabling Fine-grained Vital Sign Monitoring for Emergency Care with Handheld MmWave Radars

Fine-grained vital sign monitoring in emergency care is crucial for accurately assessing patient conditions, predicting disease progression, and formulating effective rescue plans. In non-hospital settings, limited equipment often necessitates manual observation of respiration and heartbeat, which can lead to significant errors. Contactless monitoring using wireless signals offers a promising alternative. Unlike traditional systems that require stationary devices for contactless sensing, handheld devices are more practical for rescuers during emergency care. However, sensing performance can be severely compromised by involuntary hand movements. Previous research has achieved respiration monitoring with handheld devices, but the randomness of hand motion still prevents reliable heartbeat monitoring. In this paper, we first demonstrate that the key to mitigating the effects of device motion lies in accurately estimating the motion direction. We then introduce a novel method that uses two static objects, i.e., corner reflectors, to precisely estimate the random motion direction of the device. These reflectors can be quickly and easily deployed by the rescuer before initiating vital sign monitoring, enabling a more thorough elimination of device motion effects. Comprehensive experiments validate the effectiveness of our solution using mmWave radar. Real-world tests demonstrate that our system can accurately monitor both respiration and heartbeat with handheld devices, significantly enhancing emergency medical response by improving the accuracy and feasibility of vital sign monitoring in urgent situations.

Optimal DMD Koopman Data-Driven Control of a Worm Robot.

Bio-inspired robots are devices that mimic an animal's motions and structures in nature. Worm robots are robots that are inspired by the movements of the worm in nature. This robot has different applications such as medicine and rescue plans. However, control of the worm robot is a challenging task due to the high-nonlinearity dynamic model and external noises that are applied to that robot. This research uses an optimal data-driven controller to control the worm robot. First, data are obtained from the nonlinear model of the worm robot. Then, the Koopman theory is used to generate a linear dynamic model of the Worm robot. The dynamic mode decomposition (DMD) method is used to generate the Koopman operator. Finally, a linear quadratic regulator (LQR) control method is applied for the control of the worm robot. The simulation results verify the performance of the proposed control method.

Idaho libraries to provide private spaces for SUD telehealth treatment

Called the “Third Space Project,” Idaho libraries will provide space for telehealth treatment for substance use disorders (SUDs), with an emphasis on serving rural and high‐need communities. The state Division of Behavioral Health Substance Use Disorder Unit is leading the project in partnership with the Idaho Commission for Libraries (ICfL), with funding by a $500,000 grant from the American Rescue Plan Act's 2021 Substance Use Prevention, Treatment, and Recovery Services Block Grant supplemental.

A victim risk identification model for nature-induced urban disaster emergency response.

In recent years, nature-induced urban disasters in high-density modern cities in China have raised great concerns. The delayed and imprecise understanding of the real-time post-disaster situation made it difficult for the decision-makers to find a suitable emergency rescue plan. To this end, this study aims to facilitate the real-time performance and accuracy of on-site victim risk identification. In this article, we propose a victim identification model based on the You Only Look Once v7-W6 (YOLOv7-W6) algorithm. This model defines the "fall-down" pose as a key feature in identifying urgent victims from the perspective of disaster medicine rescue. The results demonstrate that this model performs superior accuracy (mAP@0.5, 0.960) and inference speed (5.1ms) on the established disaster victim database compared to other state-of-the-art object detection algorithms. Finally, a case study is illustrated to show the practical utilization of this model in a real disaster rescue scenario. This study proposes an intelligent on-site victim risk identification approach, contributing significantly to government emergency decision-making and response.

Lessons learned from the Health Resources and Services Administration health workforce well-being grantees.

Burnout and moral injury are among the most pressing issues facing healthcare and public safety today. In 2021, Congress provided $120 million through the American Rescue Plan Act for 44 Health Resources and Services Administration grants to implement evidence-informed strategies to address burnout and improve mental health among the health workforce. This article examines facilitators and barriers to implementation and reported impact of grantees' interventions. Key informant interviews (n = 39) and surveys (n = 36) with grantees were conducted between May and August 2023 and qualitatively analyzed using inductive and deductive approaches. This study found grantees were adapting their program modality, timing, and content to meet the needs of their workforce. Many grantees were increasingly focused on training/engaging leadership, establishing structures for worker engagement, and allowing worker voice and priorities to guide operational changes. Though many grantees could not yet report program impact, those who could provide early data documented decreases in staff turnover, burnout, and moral distress. A common challenge was ongoing resource constraints, including staff and leadership turnover. Findings suggest health and public safety organizations continued to struggle with staffing post-COVID, increasing the challenges of implementing their programs to improve burnout and well-being. However, just 18 months into their activities, grantees reported their efforts were leading to changes in organizations, culture, and the experiences of individuals. Key lessons include the importance of engaging workers, building trust, and developing intentional communication, evaluation, and feedback strategies to advance organizational-level efforts to improve worker and learner well-being. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Is conversion the only itinerary to peace with "hostile" laparoscopic cholecystectomy?

Laparoscopic cholecystectomy (LC) remains the most common surgical procedure worldwide. With these unequivocal benefits, it is today the optimal standard of care for symptomatic cholelithiasis. However, this routine operation can also represent one of the most "hostile" situations. In scenarios of uncertain Critical View of Safety display, total cholecystectomy and laparoscopic approach incur a risk of serious complications such as iatrogenic bile duct injury (IBDI). In such situations, it is necessary to improvise an approach different from the traditional concepts used for elective cases and provide a rescue plan that promotes safety. Although the balance between maintaining LC and moving to an exit procedure is resolved, it remains a challenge for providers to navigate the best alternative strategy. The truth in this debate of seeking optimal results is that surgeons are inclined to resort to conversion, as the most usual itinerary. However, it is inconsistent to consider that the simple fact of switching to an open procedure necessarily protects against IBDI, as well as to approve the conversion as a single manifest alternative to cope without knowing the other saviors procedures and their profiles. Therefore, we found it very useful to compile research data regarding "hostile" CL bailout methods to optimize their use in our practice and to answer the question which strategy to choose? And why?

A Medical Student-Inspired Advocacy Effort to Improve Pediatric Mental Health Services

The demand for pediatric mental health services has exceeded the supply of mental health providers for decades and increased significantly during the COVID-19 pandemic. Given the federal administration’s interest in passing a major funding bill to address the COVID-19 pandemic, the authors recognized an opportunity to collaborate in an advocacy effort to request federal financial support for Pediatric Mental Health Care Access Programs (PMHCAPs). The challenges of the pandemic and limited travel became assets as the advocacy team, who lived hundreds of miles apart in 2 states, met virtually with each other and with Congressional staff during February of 2021 on a tight timeline. In March of 2021, President Biden signed the American Rescue Plan that provided $80 million for PMHCAPs.

Characterization of Micro-seismic Activity in Northern Cyprus Using Complexity and Corner Frequency Methods

Cyprus is an island country located in the eastern Mediterranean, to the south of Türkiye and the western of Syria and Lebanon, and is a popular tourist destination. Due to being surrounded by seas on all four sides, meticulous planning of rescue, assistance, and evacuation plans is necessary in the face of disasters such as earthquakes and tsunamis. Tectonically, the southern part of the island is controlled by the Cyprus Arc, while the northern part is dominated by the Kyrenia Range. The demand for raw materials for construction and industry is met through controlled quarry blasting operations carried out by open-pit quarry companies in the districts of Kyrenia and Nicosia. As a result, both natural and artificial seismic events occur in the region, and these quakes are documented in seismic catalogs by seismology centers. However, due to the low energy content of micro-seismic events and the inadequacy of seismic stations on the island, the source types of these seismic events can be misidentified in the catalogs. In this context, the study focuses on 122 seismic events with magnitudes between 0.9≤Ml≤2.7 that occurred in Northern Cyprus during the January 2018 - December 2021 period (4 years). The seismic events recorded by the station LFK, operated by Boğaziçi University Kandilli Observatory and Earthquake Research Institute Regional Earthquake-Tsunami Monitoring Center (KOERI-RETMC), were classified using Linear and Quadratic Discriminant Functions based on complexity and corner frequency methods. According to the results obtained, 10 of the 122 seismic events were identified as natural, and 96 were determined to be artificial, resulting in a general success rate of 86.89%. However, classification results for 16 seismic events were inconclusive with the methods used. As a result, more detailed secondary analyses should be conducted to accurately determine the source types of micro-seismic events, and the seismic catalogs should be updated accordingly.

Multiple Object Tracking in Drone Aerial Videos by a Holistic Transformer and Multiple Feature Trajectory Matching Pattern

Drone aerial videos have immense potential in surveillance, rescue, agriculture, and urban planning. However, accurately tracking multiple objects in drone aerial videos faces challenges like occlusion, scale variations, and rapid motion. Current joint detection and tracking methods often compromise accuracy. We propose a drone multiple object tracking algorithm based on a holistic transformer and multiple feature trajectory matching pattern to overcome these challenges. The holistic transformer captures local and global interaction information, providing precise detection and appearance features for tracking. The tracker includes three components: preprocessing, trajectory prediction, and matching. Preprocessing categorizes detection boxes based on scores, with each category adopting specific matching rules. Trajectory prediction employs the visual Gaussian mixture probability hypothesis density method to integrate visual detection results to forecast object motion accurately. The multiple feature pattern introduces Gaussian, Appearance, and Optimal subpattern assignment distances for different detection box types (GAO trajectory matching pattern) in the data association process, enhancing tracking robustness. We perform comparative validations on the vision-meets-drone (VisDrone) and the unmanned aerial vehicle benchmarks; the object detection and tracking (UAVDT) datasets affirm the algorithm’s effectiveness: it obtained 38.8% and 61.7% MOTA, respectively. Its potential for seamless integration into practical engineering applications offers enhanced situational awareness and operational efficiency in drone-based missions.

Nurse Corps’ impact on increasing registered nurse staffing in critical shortage areas and facilities, 2017 to 2022

BackgroundThe registered nurse (RN) workforce experienced critical pre-pandemic and pandemic shortages of labor in some areas in the United States. People living in these health professional shortage areas (HPSAs) may have less access to health services. The Bureau of Health Workforce within the Health Resources and Services Administration administers Nurse Corps scholarship and loan repayment programs to increase healthcare access by increasing the supply and distribution of RNs, nurse practitioners, and nurse faculty to HPSAs. The American Rescue Plan Act of 2021 (ARPA) made available considerable new resources for the program. PurposeThis paper reports on Nurse Corps applications, awards, and distribution in 2 cohorts in the period 2017 to 2022 to assess the impact of receiving an additional $200 million appropriated in 2021. DiscussionAdditional funds through ARPA were associated with nearly threefold increases in the number of Nurse Corps awards. Program participants worked in a total of 1,316 counties (42% of all U.S. counties) in 2020 to 2022, a 76% increase from 749 counties in 2017 to 2019. ConclusionIncreased funding for scholarship and loan repayment can help to improve the distribution of nurse labor to a greater number of critical shortage areas in the United States.

Study on temperature field beneath tunnel ceiling induced by transverse symmetrical double fires

During a tunnel fire incident, a substantial volume of high-temperature smoke gathers and disperses beneath the tunnel ceiling, posing significant risks of casualties and structural harm. A comprehensive comprehension of the temperature distribution of smoke beneath the ceiling is pivotal for the fire-resistant design and formulation of rescue plans for tunnel structures. This paper initially employs the FTP theory to analyze the fire scene resulting from fire spread under vehicle congestion. Subsequently, A set of numerical simulations is performed, and a prediction model of the temperature field for transverse symmetrical double fires is established according to the simulation results. Finally, an air entrainment model for transverse symmetrical double fires is proposed. The air entrainment amount under different fire spacing is obtained by theoretical derivation. Combined with the relationship between air entrainment amount, flame height, and maximum temperature, the variation of temperature field under the ceiling with fire spacing is explained from the mechanism. The results show that: the maximum temperature beneath tunnel ceiling increases with the increase of the fire power, decreases and then increases with the increase of the fire spacing and the variation has symmetry; The temperature attenuation along transverse and longitudinal directions under the ceiling following the exponential attenuation model, and the temperature attenuation rate is positively correlated with the maximum temperature. The transverse temperature attenuation is smaller than that of the longitudinal temperature under the same conditions; There is an internal relationship between the air entrainment, flame height and temperature field of the transverse double fires. The variation law of the temperature field beneath the ceiling depends on the degree of air entrainment limitation. Based on mirror model, this paper indicates that when the fire spacing is within a certain range, the combustion of the double fires can be effectively represented as near-wall fire.

Landslide Detection Using the Unsupervised Domain-Adaptive Image Segmentation Method

After a landslide, swift and precise identification of the affected area is paramount for facilitating urgent rescue operations and damage assessments. This is particularly vital for land use planners and policymakers, enabling them to efficiently address hazard mitigation, the resettlement of those affected by the hazards, and to strategize land planning in the impacted regions. Despite the importance, conventional methods of monitoring landslides often fall short due to their restricted scope and the challenges associated with data acquisition. This study proposes a landslide detection method based on unsupervised multisource and target domain adaptive image segmentation (LUDAS) that is capable of achieving robust and generalized landslide mapping across multiple sources and target domains. Specifically, LUDAS consists of two phases. In the first phase, we introduce an unsupervised interdomain translation network to align the styles of multiple source domains to multiple target domains, generating pseudotarget domain data. Our interdomain translation network is capable of style transfer between any two domains. Through careful design of the network structure and loss functions, we ensure effective style transfer while preserving the content structure of the source domain images. In the second phase, the landslide segmentation model is trained in a supervised manner using annotated data from multiple source domains and multiple pseudotarget domains, resulting in a model with strong generalization capabilities that can adapt to multiple source and target domains. Finally, through extensive qualitative and quantitative analysis experiments, our study confirms that the proposed domain-adaptive segmentation model not only achieves exceptional landslide segmentation performance across multiple target domains but also, due to its good generalizability and transferability, has great potential for application in the emergency response to landslide. This capability can provide strong support for post-disaster emergency rescue, disaster assessment, and land planning in areas with scarce data.

A policy window for equity? The American Rescue Plan and local government response

ABSTRACT How have local governments incorporated equity into their investments of state and local rescue funds from the American Rescue Plan Act? We explore the multiple mechanisms localities used to incorporate equity approaches through case studies based on a strategic equity framework of redistributional, procedural and conceptual equity. We identify the importance of political will, planning and strategic partnerships with community agencies and businesses to experiment with new initiatives, and feedback loops with higher levels of government for policy change. Equity was often hidden in program design, rather than explicitly articulated. We find that cities are leading with actions, while values still primarily focus on economic development benefits. However, these actions may subtly shift values toward a broader conception of the local government role in advancing equity.

DEA-driven risk management framework for oil supply chains

This paper develops a comprehensive risk management framework to optimise the Nigerian oil supply chain risk mitigation strategies. Given the complexities of the oil supply chain, this framework aids researchers and decision-makers in navigating innovative risk management approaches and considering the implementation of associated challenges. Data Envelopment Analysis (DEA) is initially employed to prioritise risk factors, and based on obtained results, optimal response strategies are determined. The study's findings reveal that criminality and terrorist attacks are the paramount Nigerian oil supply chain risk factors. Additionally, the research identifies practical mitigation strategies, such as risk transfer, safety planning, alternative energy carriers, improved energy efficiency, emergency rescue plans, expected shortages, and diplomatic relations. This research contributes valuable insights to both academia and industry, fostering a deeper understanding of risk dynamics in the Nigerian oil sector. The framework presented offers a powerful approach to risk management, providing a foundation for informed decision-making and strategic planning in the ever-evolving landscape of supply chain dynamics.