High-priority Areas Research Articles

Identifying High-Priority Ethical Challenges for Precision Emergency Medicine: Nominal Group Study.

Precision medicine promises to revolutionize health care by providing the right care to the right patient at the right time. However, the emergency department's unique mandate to treat "anyone, anywhere, anytime" creates critical tensions with precision medicine's requirements for comprehensive patient data and computational analysis. As emergency departments serve as health care's safety net and provide a growing proportion of acute care in America, identifying and addressing the ethical challenges of implementing precision medicine in this setting is crucial to prevent exacerbation of existing health care disparities. The rapid advancement of precision medicine technologies makes it imperative to understand these challenges before widespread implementation in emergency care settings. This study aimed to identify high priority ethical concerns facing the implementation of precision medicine in the emergency department. We conducted a qualitative study using a modified nominal group technique (NGT) with emergency physicians who had previous knowledge of precision medicine concepts. The NGT process consisted of four phases: (1) silent generation of ideas, (2) round-robin sharing of ideas, (3) structured discussion and clarification, and (4) thematic grouping of priorities. Participants represented diverse practice settings (county hospital, community hospital, academic center, and integrated managed care consortium) and subspecialties (education, ethics, pediatrics, diversity, equity, inclusion, and informatics) across various career stages from residents to late-career physicians. A total of 12 emergency physicians identified 82 initial challenges during individual ideation, which were consolidated to 48 unique challenges after removing duplicates and combining related items. The average participant contributed 6.8 (SD 2.9) challenges. These challenges were organized into a framework with 3 themes: values, privacy, and justice. The framework identified the need to address these themes across 3 time points of the precision medicine process: acquisition of data, actualization in the care setting, and the after effects of its use. This systematic organization revealed interrelated concerns spanning from data collection and bias to implementation challenges and long-term consequences for health care equity. Our study developed a novel framework that maps critical ethical challenges across 3 domains (values, privacy, and justice) and 3 temporal stages of precision medicine implementation. This framework identifies high-priority areas for future research and policy development, particularly around data representation, privacy protection, and equitable access. Successfully addressing these challenges is essential to realize precision medicine's potential while preserving emergency medicine's core mission as health care's safety net.

Understanding good communication in ambulance pre-alerts to the emergency department: findings from a qualitative study of UK emergency services

ObjectivesPrehospital notifications (pre-alerts) enable emergency department (ED) staff to prepare for the arrival of patients requiring a time-critical response. Effective communication of the pre-alert is key to enabling the ED...

The Use of PhET Simulations in Evaluating Students’ Level of Cognitive Skills Utilizing Solo Taxonomy

Background and Aims: Science is frequently regarded as crucial due to its links to technology and industry, which, from a national viewpoint, may be one of the areas of high priority for research, innovation and development. As digital technology emerges as a key strategy in education, this study aims to explore the effectiveness of PhET Simulations on the Grade 8 students' cognitive skills in learning essential topics in force and motion. Methodology: The study used a mixed-methods approach, combining a quasi-experimental design with a one-group pretest-posttest for quantitative data and a sequential exploratory design for qualitative insights. The study focused on how PhET Simulations in science teaching influenced the students' cognitive skills. Qualitative data gathered through students' feedback and perceptions were analyzed using thematic analysis. Results: Findings revealed a significant improvement in the student's cognitive skills, with a shift from a prestructural level (mean score 1.51) to a multistructural level (mean score 3.64) in the first trial and from a prestructural level (mean score 1.20) to a multistructural level (mean score 3.92) in the second trial. Statistical analysis confirmed these improvements with a significant p-value below 0.05. These results highlight the effectiveness of PhET Simulations in science education, showing that with the right tools and approaches, students can significantly advance their understanding and develop cognitive skills. Conclusion: It was concluded that PhET Simulation is an effective tool for developing cognitive skills, thus highlighting notable progress in understanding complex concepts, increased curiosity and exploration, and boosted game-based features. While challenges such as limited access to technology, outdated versions of the gadget's OS, difficulty navigating the simulations, and the need for foundational knowledge were identified, addressing these issues could unlock the full potential of simulation, making it a highly effective tool that fosters deeper learning.

A spatial triage of at-risk conifer forests to support seed collection efforts and sustainable forestry.

At-risk conifer stands growing in hot, arid conditions at low elevations may contain the most climate change-adapted seeds needed for sustainable forestry. This study used a triage framework to identify high-priority survey areas for Pinus ponderosa (Pipo) within a large region, by intersecting an updated range map with a map of seed zones and elevation bands (SZEBs). The framework assesses place-based climate change and potential wildfire risks by rank-order across 740 potential collection units. The study separately combined three operational measures of cone priority SEZBs from a government reforestation nursery in California - current inventory, target seed supply levels, and areas with high seed demand - to create operational SZEB priority rankings. Combining the risk and operational SZEB rankings permitted an overall priority ranking of survey areas, and road extents within each SZEB's Pipo area indicate accessibility. Pipo's California range covers 62,456.9km2, which intersects 740 of 1212 total SZEBs. Of these, 43 have high climate exposure under baseline (1980-2010) conditions, and 139 more become highly exposed by the end of the century. Of these 182 highly climate-exposed SZEBs, our index of high-intensity fire risk indicates 42 were also at high risk of stand replacing fire at the beginning of the 2023 fire season. Of these 42, only 4 are currently represented in the seed lot inventory. In contrast, the top 73 operational priority SZEBs all have relatively low risk rankings. Integrating models of landscape risk with operational seed collection priorities can direct collection efforts to high-risk stands before those are lost, and improve spatial coverage in seed bank inventories. The triage framework can provide spatial guidance for cone crop surveying efforts, and has the potential to improve forest nursery and field management. Triage elements can be updated or added to provide more comprehensive tracking over time. For example, wildfire perimeters and seed inventories could be updated annually and fuels reduction treatments, cone crop survey routes, annual assessments of cone crop condition, and additional seedbanks (e.g. for the USDA Forest Service) could be included.

Universal newborn screening for congenital cytomegalovirus infection.

Congenital cytomegalovirus (CMV) infection is the leading infectious cause of childhood disability, in particular sensorineural hearing loss (SNHL). Timeliness of diagnosis is crucial, since the presence of CMV in any compartment (eg, blood, urine, or saliva) after age 21 days can mean postnatal acquisition of infection, particularly in breastfed infants. Given these issues, there is considerable interest in implementation of screening programmes-either universal screening (where all newborns are tested) or targeted screening. Targeted screening is typically based on the outcome of a newborn hearing screen, and can be influenced in some strategies by findings of other signs suggestive of congenital CMV. Universal screening is likely to have the greatest overall benefit. Early identification of congenital CMV allows for interventions such as antiviral therapy (when indicated) and enables anticipatory audiological monitoring that facilitates timely detection of delayed-onset SNHL. However, there are debates about the effectiveness of screening programmes. Most infants with congenital CMV are unaffected and do not appear to be at risk for adverse neurodevelopment outcomes, except for SNHL. Screening can, therefore, raise unwarranted concern among parents and clinicians in these cases. The best clinical sample for diagnostic testing is unclear. PCR testing of saliva is sensitive but has a risk of yielding false-positive results in infants without congenital CMV. Resolving the technological issues has improved the sensitivity of dried blood spot (DBS) PCR but the technique remains suboptimum. An advantage to DBS PCR testing is that an infrastructure exists to add this test to existing newborn screening programmes. In this Review, the advantages and disadvantages of congenital CMV screening are discussed, along with high-priority areas for future research that will inform and direct this rapidly evolving field.

Service Quality Assessment of Ngurah Rai International Airport Using Fuzzy Servqual and Importance Performance Analysis Methods

Ngurah Rai International Airport in Bali serves as a critical gateway for tourists, especially in the post-pandemic era. This study evaluates the service quality of the airport using the Fuzzy Servqual and Importance Performance Analysis (IPA) methods. A sample of 100 respondents was analyzed to assess five dimensions of service quality: tangibles, reliability, responsiveness, assurance, and empathy. The findings indicate that several attributes of the service require improvement, particularly in terms of adequacy of infrastructure, service clarity, and responsiveness to passenger feedback. The IPA analysis identified two attributes as high-priority areas for improvement. Recommendations for enhancing service quality are proposed to better align with passenger expectation and satisfaction.

A Community-Engaged, Mixed-Methods Approach to Prioritizing Needs in a Statewide Assessment of Community Cancer Needs.

Kentucky has the highest all-site cancer incidence and death rate in the US. In 2021, the University of Kentucky Markey Cancer Center convened a steering committee to conduct a statewide community cancer needs assessment (CNA). The goal of the final CNA phase was to gather community input on prioritizing Kentucky's cancer-related needs and ways to address them. In 2021, we recruited 162 people to participate in online concept mapping, a participatory mixed method, to explore connections and identify priority areas. Fifty-one community members and 111 organizational partners participated in survey-based activities to prioritize 80 items representing key CNA findings and discussion groups to explore key focus areas and strategies for Kentucky communities. Concept maps display perceived similarity of the 80 items and a 6-cluster solution. High-priority focus areas included lung cancer screening, smoking, human papillomavirus (HPV) vaccination, and disparities driven by social determinants among rural, Appalachian, Black, and Hispanic residents. High-priority strategies to address needs included expanding health communication on risks, screening guidelines, and insurance benefits; patient navigation; accessible, culturally appropriate treatment information and self-efficacy in treatment decisions; access to care through financial assistance, mobile clinics, and at-home screening; and patient-provider trust and communication. Our findings indicate the utility of the concept mapping process to facilitate the prioritization of wide-ranging catchment area needs and ways to address them. Moving forward, the prioritized focus areas and strategies can inform Kentucky's new state cancer plan and future research to reduce the state's cancer burden and disparities.

Prioritizing Research for Enhancing the Technology Readiness Level of Wind Turbine Blade Leading-Edge Erosion Solutions

An enhanced understanding of the mechanisms responsible for wind turbine blade leading-edge erosion (LEE) and advancing technology readiness level (TRL) solutions for monitoring its environmental drivers, reducing LEE, detecting LEE evolution, and mitigating its impact on power production are a high priority for all wind farm owners/operators and wind turbine manufacturers. Identifying and implementing solutions has the potential to continue historical trends toward lower Levelized Cost of Energy (LCoE) from wind turbines by reducing both energy yield losses and operations and maintenance costs associated with LEE. Here, we present results from the first Phenomena Identification and Ranking Tables (PIRT) assessment for wind turbine blade LEE. We document the LEE-relevant phenomena/processes that are deemed by this expert judgment assessment tool to be the highest priorities for research investment within four themes: atmospheric drivers, damage detection and quantification, material response, and aerodynamic implications. The highest priority issues, in terms of importance to LEE but where expert judgment indicates that there is a lack of fundamental knowledge, and/or implementation in measurement, and modeling is incomplete include the accurate quantification of hydrometeor size distribution (HSD) and phase, the translation of water impingement to material loss/stress, the representation of operating conditions within rain erosion testers, the quantification of damage and surface roughness progression through time, and the aerodynamic losses as a function of damage morphology. We discuss and summarize examples of research endeavors that are currently being undertaken and/or could be initiated to reduce uncertainty in the identified high-priority research areas and thus enhance the TRLs of solutions to mitigate/reduce LEE.

Spatial patterns of colorectal cancer survival rates in Malaysia, 2013-2018.

Large geographical variations in colorectal cancer (CRC) survival rates have been reported across regions. Poorer survival rates were mainly found in socioeconomically deprived areas, highly dense areas, and areas lacking healthcare accessibility. The objective of this study was to identify, compare, and contrast the spatial patterns of 5-year CRC-specific survival rates and identify high-priority areas by districts in Malaysia. This retrospective cohort study utilized secondary data from the National Cancer Registry. CRC patients (ICD10 C18-21) diagnosed between 2013 and 2018 were selected. Patient addresses were geocoded into districts and states via geospatial data from the National Geospatial Centre, whereas district population density data were gathered from the Population Census of Malaysia. Kaplan‒Meier survival analysis and log-rank test were conducted to determine and compare the 5-year CRC-specific survival rates, and the spatial distribution of CRC survival by district was determined via ArcGIS software. A total of 18,513 CRC patients were registered from 143 districts, with 10,819 deaths occurring during follow-up. The national 5-year CRC-specific survival rate was 42%, with median survival time of 36months (95% CI: 34.46, 37.54). The eastern region (Kelantan, Terengganu, and Pahang) had the lowest survival (38.0%). Among the 143 districts, eighty-one (56.6%) reported survival rates below the national average while thirty-six (25.2%) were identified as high-priority districts. The differences in CRC survival rates were evident according to geographical location. Area-based targeted interventions to improve CRC detection, management, and access to healthcare are imperative to address cancer survival disparities and help effectively allocate resources.

Revealing future effectiveness of protected areas for biodiversity conservation in the Guangdong-Hong Kong-Macao Greater Bay Area

The biodiversity of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) is increasingly under threat due to anthropogenic stressors and climate change challenges, despite 15.72% of the landscape being planned as protected areas (PAs). The uncertain risks of high-density urban sprawl and sea level rise pose challenges for future biodiversity conservation in the GBA. To effectively gauge the impact of PAs for biodiversity conservation, it’s imperative to delve into not only the pattern but the process of biodiversity. Adopting a dynamic view for biodiversity assessment, our study established a synergistic approach within a systematic conservation planning framework, focusing on comprehensively assessing the future conservation effectiveness of PAs. Four modeling techniques were integrated in the process to estimate conservation priorities under various future scenarios: SLAMM and Dyna-CLUE projected future land-use changes, MaxEnt predicted shifts in habitat suitability for key species, and Zonation identified conservation priorities thereby. The results indicated a potential shift in conservation priorities over time from inland to coastal areas across different cities in the GBA, highlighting the substantial mismatches between current PAs and high-priority areas in Guangdong and the urgency for prompt conservation actions. Moreover, our findings revealed that proposed PA system has not sufficiently prioritized wetland conservation, nor has it effectively conserved amphibian, plant and bird species. Our study provided a dynamic and comprehensive evaluation of biodiversity in the GBA and offered insightful conservation recommendations, thus demonstrating a viable approach for assessing and enhancing future conservation initiatives in similar contexts.

A novel comprehensive approach to soil and water conservation: integrating morphometric analysis, WSA, PCA, and CoDA-PCA in the Naama sub-basins case study, Southwest of Algeria.

This research paper presents a detailed investigation into the morphometric characteristics of sub-basins within the Naama region of Algeria, aiming to prioritize areas vulnerable to soil erosion and runoff risks. Focusing on five key sub-basins that collectively represent 75% of the Wilaya of Naama, the study employs a comprehensive methodological framework, integrating morphometric analysis (MA), weighted sum analysis (WSA), principal component analysis (PCA), and the novel approach of compositional data analysis (CoDA). Through the rigorous evaluation of sixteen distinct morphometric parameters selected based on their relevance to hydrological and geomorphological processes that influence erosion and runoff, this research provides a nuanced understanding of the factors influencing erosion susceptibility within each sub-basin. The analysis reveals a clear hierarchy of sub-basins based on their calculated compound parameters, effectively classifying them into high, moderate, and low priority categories for targeted intervention and resource allocation. The results highlight the Ain Sefra and Wadi Er Rosafa sub-basins as the highest priority areas, collectively encompassing 31.51% of the wilaya and posing the most significant threats of runoff and soil erosion. This identification allows for the prioritization of conservation efforts and the implementation of tailored management strategies in these critical areas. Furthermore, the integration of multiple prioritization approaches, including the innovative application of CoDA, ensures a robust and comprehensive assessment of the sub-basin landscapes. This multi-faceted approach provides a more nuanced understanding of the complex interplay between various morphometric parameters and their influence on erosion and runoff potential. The findings of this research have significant implications for sustainable land and water resource management within the Naama region. By identifying and prioritizing vulnerable sub-basins, the study provides a crucial foundation for informed decision-making, enabling stakeholders to implement targeted interventions and mitigate the detrimental impacts of soil erosion and excessive runoff. Moreover, the methodological framework presented in this research paper offers a valuable blueprint for similar studies in other regions facing comparable challenges. The cost-effective and time-efficient nature of the approach makes it a practical tool for prioritizing erosion and runoff risks in arid and semi-arid environments worldwide, contributing to the broader goals of environmental sustainability and land degradation neutrality.

Nurses' reported training needs for advanced cell therapies: a survey on behalf of the Nurses Group of the EBMT.

Advanced Therapy Medicinal Products (ATMPs) for human use have advanced globally with the rapid adoption of Chimeric Antigen Receptor T-cell (CAR-T) therapies in haemato-oncology. CAR-T cell therapy and ATMPs have unique, significant acute and chronic toxicities, and appropriate patient care is crucial. Significant challenges, including the need for nurse education and training, accompany optimal patient success and benefits. This study aimed to describe nurses' training needs in relation to ATMP management and patient care. A cross-sectional online survey was performed by the European Society for Blood and Marrow Transplantation, based on a previously tested questionnaire developed in the UK. 109 complete responses from 86 different centers from 24 countries were returned (1207 distributed). Over 1/3 reported experience delivering licensed ATMPs (CAR-T). High-priority training areas included a general introduction to ATMPs, toxicity management, product-specific information, and regulatory frameworks for ATMPs. A clear need for ATMP-specific training exists and is regarded as important. Training prior to implementation is key and should be supported by ongoing competency maintenance. Counseling, patient support, and long-term follow-up are identified for future training and opportunities for nurse experience sharing in this rapidly evolving field.

Researcher identities and values in the impact agenda: the case of artificial intelligence academics

AbstractA major shift in the research sector has been the increased expectation from policymakers and funders that academic research should yield some socioeconomic benefits or ‘impacts’ rather than merely new knowledge. In this paper, we explore the role that impact has in academics’ motivations and values and how impact is being integrated into academics’ core functions of research and education. We do this through in-depth interviews (n = 60) with scientists who work on the development or application of artificial intelligence (AI), broadly defined. This AI’s focus situates our participants within a strategically important, high-priority area of research for all three national contexts included in our study—Australia, the UK and the USA. Our findings reveal that the impact mission has become central to understanding the motivations and values of academics, but unevenly. We identify divergence between those who work on AI from a foundational computer science perspective and those who develop and apply AI within other scientific domains. The two groups have different understandings of key notions such as ‘impact’ and ‘applied research’, as well as different ways of integrating the impact agenda into their research and education activities. The study highlights the importance of flexible approaches to research policy and governance that are based on a deeper understanding of what motivates researchers, and that take into account academics’ educational role. Greater holistic understanding of how academic identities and practices are accommodating the impact agenda is essential to maximise synergy across activities and avoid unintended consequences.

Telehealth Research Strategies in a Post COVID-19 Era: A Roadmap for the Coming Decade.

An Agency for Healthcare Research and Quality-funded Think Tank, held at Michigan State University, brought together a wide range of subject matter experts in telemedicine, telehealth, digital health, digital access, and health care. The authors of this article represented a group focused on the research needs, constructs, and strategies in the post-COVID era. While telemedicine and telehealth grew exponentially during the pandemic, the challenges that have been with us for decades, while ameliorated to some extent, remain. To showcase the State of Michigan as a model, the authors reviewed challenges and opportunities related to telehealth and telemedicine usage and categorized them into seven areas of highest priority. Based on a review of the literature and consultation in the fields of telehealth, telemedicine, and digital access, we identified seven key categories where research would be most effective going forward. These categories include research into the impact of telehealth on clinical services, telehealth's use in administrative activities, education and training for health care providers and patients, telehealth policy implications at state and federal levels, the impact of future technology and innovation on telehealth services, patient characteristics and their experiences, and the ethical aspects of telehealth in the future. We have formulated this overview of our findings to act as a roadmap for future telehealth research. We recommend that ongoing studies should explore each of the seven categories identified above. The search for solutions to overcome the challenges within these topics must not be constrained to research that has been conducted recently; many of these challenges have faced telehealth researchers for decades, and numerous solutions have been proposed over the years. Some of these proposals should be explored once again in light of technological and societal advances. The findings from these studies should be shared in ways and through venues that can make them accessible outside the spheres of academic research, but also by practitioners, policymakers, and patients. Third, research into telehealth in all its incarnations must be prioritized, and leadership is needed to ensure these areas of study are continued to be spotlighted after the noise of the COVID-19 pandemic grows quiet.

Integrated spatial prioritization of urban nature-based solutions for climate adaptation, mitigation, and justice

ABSTRACT In densely populated cities, people confront multiple impacts of climate change, including stormwater runoff, flooding, heat waves, and health issues. To address these impacts, nature-based solutions (NbS) have gained significant attention in recent years due to their potential to contribute to sustainability, resilience, and ecosystem services. Nevertheless, more research is required to understand the wider potential of NbS and to identify priority areas for their deployment. This study developed an analytical approach and implemented it in the Tehran Metropolitan Area (TMA) by considering factors related to NbS co-benefits. The approach included spatial prioritization, spatial correlation analysis, and suitability analysis. It combines a Geographic Information System, Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, Zonation, Best-Worst Method, Bivariate Local Indicator of Spatial Autocorrelation analysis (BiLISA), a Fuzzy Inference System, and Boolean overlay analysis. The results include maps showing priority areas for different types of NbS and spatial co-benefits and mismatches. High-priority areas are located in disadvantaged areas. In total, 105.73 km2 (17% of the TMA) was designated as highly prioritized. We show that various NbS interventions are applicable, with green roofs and rainwater harvesting being the most feasible. This study can assist decision-makers in optimizing NbS to deliver maximum co-benefits.

Patterns of Adverse Childhood Experiences Among Transitional-Age Youth Experiencing Homelessness: A Cluster Analysis.

Transitional-age youth experiencing homelessness (TAY-EH) represent a high-risk population with substantial exposure to adverse childhood experiences (ACEs) and high rates of morbidity and mortality. The current study uses a person-centered analytical approach to identify patterns of ACEs among TAY-EH and inform more nuanced characterizations of this vulnerable group. Participants were aged 16-25 years accessing support at a psychosocial agency in Boston, Massachusetts. Data were gathered via structured assessment. Hierarchical agglomerative cluster analysis identified subgroups of TAY-EH with differing ACE exposures. Within the sample of 138 TAY-EH, three unique clusters of ACE exposures were identified: two high-occurrence groups (one also endorsing sexual abuse and one not), and one lower-occurrence group. Clusters differed significantly on several variables including gender identity, sexuality, and suicidality. The ways in which trauma phenotypes inform treatment access, utilization patterns, and functional outcomes should be high-priority areas for ongoing research in TAY-EH and related marginalized youth populations.

Optimizing afforestation and reforestation strategies to enhance ecosystem services in critically degraded regions

Human activity has caused massive forest ecosystem damage, threatening the global environmental balance. Afforestation and reforestation are crucial strategies for the restoration of forest ecosystem functions. This study was conducted on Belitung Island, Indonesia, which has experienced forest degradation due to mining activity and is currently undergoing forest restoration efforts. This study aimed to identify priority areas for afforestation and reforestation using an innovative approach that integrates multi-criteria analysis (MCA) and machine-learning techniques based on ecosystem service (ES) indicators, wildfire susceptibility, and environmental pressure. This study is the first to combine long-term remote sensing data with machine learning to develop priority scenarios for afforestation and reforestation. Results show that low-priority afforestation areas cover 24,479.66 ha (20.45 %), medium-priority areas 58,703.30 ha (49.04 %), and high-priority areas 36,521.98 ha (30.51 %). For reforestation, low-priority areas cover 23,123.45 ha (30.45 %), medium-priority areas 38,197.36 ha (50.3 %), and high-priority areas 14,618.27 ha (19.25 %). This study is expected to serve as a reference for sustainable forest ecosystem restoration efforts in various regions by leveraging ES approaches and environmental conditions using remote-sensing technology.

Developing Evidence to Support Policy: Protocol for the StrAtegic PoLicy EvIdence-Based Evaluation CeNTer (SALIENT).

All federal agencies are required to support appropriation requests with evidence and evaluation (US Public Law 115-435; the Evidence Act). The StrAtegic PoLicy EvIdence-Based Evaluation CeNTer (SALIENT) is 1 of 6 centers that help the Department of Veterans Affairs (VA) meet this requirement. Working with the existing VA evaluation structure, SALIENT evaluations will contribute to (1) optimize policies and programs for veteran populations; (2) improve outcomes regarding health, equity, cost, and provider well-being; (3) advance the science of dissemination and knowledge translation; and (4) expand the implementation and dissemination science workforce. We leverage the Lean Sprint methodology (iterative, incremental, rule-governed approach to clearly defined, and time-boxed work) and 3 cores to develop our evaluation plans collaboratively with operational partners and key stakeholders including veterans, policy experts, and clinicians. The Operations Core will work with evaluation teams to develop timelines, facilitate work, monitor progress, and guide quality improvement within SALIENT. The Methods Core will work with evaluation teams to identify the most appropriate qualitative, quantitative, and mixed methods approaches to address each evaluation, ensure that the analyses are conducted appropriately, and troubleshoot when problems with data acquisition and analysis arise. The Knowledge Translation (KT) Core will target key partners and decision makers using a needs-based market segmentation approach to ensure that needs are incorporated in the dissemination of knowledge. The KT Core will create communications briefs, playbooks, and other materials targeted at these market segments to facilitate implementation of evidence-based practices and maximize the impact of evaluation results. The SALIENT team has developed a center infrastructure to support high-priority evaluations, often to be responsive to shifting operational needs and priorities. Our team has engaged in our core missions and operations to rapidly evaluate a high-priority areas, develop a comprehensive Lean Sprint systems redesign approach to training, and accelerate rapid knowledge translation. With an array of interdisciplinary expertise, operational partnerships, and integrated resources, SALIENT has an established and evolving infrastructure to rapidly develop and implement high-impact evaluations. Projects are developed with sustained efficiency approaches that can pivot to new priorities as needed and effectively translate knowledge for key stakeholders and policy makers, while creating a learning health system infrastructure to foster the next generation of evaluation and implementation scientists. PRR1-10.2196/59830.

Nature-Based Solutions Scenario Planning for Climate Change Adaptation in Arid and Semi-Arid Regions

Extreme climatic conditions cause a decrease in ecosystem services, the disruption of the ecological balance, and damage to human populations, especially in areas with socially vulnerable groups. Nature-based solutions applying blue-green infrastructure (BGI) against these negative impacts of climate change have an important role in planning sustainable cities. This study aims to identify priority areas and develop scenarios and strategies for spatial planning to understand the tradeoffs in approaches and to maximize the benefits of ecosystem services provided by BGI in cities with arid and semi-arid climates, using Phoenix, Arizona, a swiftly urbanizing city in the Sonoran Desert, as the study area. Using GIS-based multi-criteria decision-making techniques and the Green Infrastructure Spatial Planning model integrated with the city’s existing water structures, this study is conducted at the US census scale. The hotspots for BGI are mapped from the combined GIS-based multi-criteria evaluation and expert stakeholder-driven weighting. In the hotspots where priority areas for BGI in Phoenix are identified, the city center area with a high density of impervious surfaces is identified as the highest priority area. It is revealed that social vulnerability and environmental risks (flooding, heat) have a positive correlation in Phoenix, and stormwater management and the urban heat island are the criteria that should be considered first in BGI planning.

Determining research priorities using machine learning

We summarize our exploratory investigation into whether Machine Learning (ML) techniques applied to publicly available professional text can substantially augment strategic planning for astronomy. We find that an approach based on Latent Dirichlet Allocation (LDA) using content drawn from astronomy journal papers can be used to infer high-priority research areas. While the LDA models are challenging to interpret, we find that they may be strongly associated with meaningful keywords and scientific papers which allow for human interpretation of the topic models.Significant correlation is found between the results of applying these models to the previous decade of astronomical research (“1998–2010” corpus) and the contents of the Science Frontier Panels report which contains high-priority research areas identified by the 2010 National Academies’ Astronomy and Astrophysics Decadal Survey (“DS2010” corpus). Significant correlations also exist between model results of the 1998–2010 corpus and the submitted whitepapers to the Decadal Survey (“whitepapers” corpus). Importantly, we derive predictive metrics based on these results which can provide leading indicators of which content modeled by the topic models will become highly cited in the future. Using these identified metrics and the associations between papers and topic models it is possible to identify important papers for planners to consider.A preliminary version of our work was presented by Thronson et al. (2021) and Thomas et al. (2022).