Impact Metrics Research Articles

Empowering Access to Justice through Automated Legal Case Management

This article presents an analytical framework for understanding automated legal case management systems, specifically focusing on Pega's implementation across public defense organizations. A comprehensive examination of system architecture and empirical evidence gathered from multiple jurisdictions demonstrates how workflow automation and intelligent case routing can substantially enhance case processing efficiency while reducing operational costs. This article examines implementation patterns across immigration, family law, and public defense sectors, revealing significant improvements in case management efficiency. This article indicates that automated case management systems markedly improve access to legal services for underserved communities, demonstrated through substantial reductions in case backlogs and increased case handling capacity. A novel framework is used to evaluate legal automation systems that consider technical performance and societal impact metrics. This article extends the current understanding of legal technology implementation by providing detailed insights into system architecture, data flow optimization, and social impact assessment. This article reveals that properly implemented automation systems can transform public legal services, creating more equitable access to justice while maintaining high standards of legal service delivery

Real-Time Environmental Design Parameters and their Impact on the Performance of Passive Basin Solar Still: A Dynamic CFD Modeling Approach

A computational fluid dynamics (CFD) approach was used in this work to investigate various design and environmental factors and their impact on the performance of passive basin solar stills (PBSS). This requires using the ANSYS software to build a sophisticated geometrical solar still model with the associated solar cells. The impact of solar cell configuration on energy optimization was determined by analyzing the developed system in both series and parallel configurations. Accuracy of simulation results was ensured in the model by introducing a meshing technique that consisted of>2 million components. The results (for static pressure analysis) indicated the absence of any high-pressure-induced strains after evaluating the performance metrics (static pressure, velocity, and energy distribution) for impact on the system using > 200 iterations; this suggests that the system design is balanced. Hence, the performance of solar stills can be improved by using new materials and design configurations as shown in this work. This study has improved knowledge of solar water desalination and other forms of renewable energy sources for related applications.

Seductive emails, dangerous consequences: how predatory journals, conferences, and publishers target early-career researchers.

In recent years, early-career researchers and medical students have become targets of predatory publishers and conference organizers. These groups exploit the pressure to publish and gain recognition. They often offer tempting opportunities, such as fast publication, free travel, and prestigious positions. However, these offers often hide a profit-driven agenda. They lack academic integrity. Many of these events have little or no peer review. The impact metrics they claim are often inflated, and event details are vague. This paper aims to highlight the rise of predatory journals and conferences. It focuses on the harmful effects these organizations have on young researchers. We will identify the tactics they use. We will also point out key red flags. The goal is to help researchers, particularly medical students, understand how to navigate academic publishing and protect their careers. Predatory publishers and conferences pose a growing threat to academic integrity. Young researchers are especially vulnerable to these deceptive practices. It is important for academics to recognize warning signs. They should focus on quality over quantity when publishing. Educating the next generation of researchers is essential for preserving academic credibility.

Insights from the international workshop on "Adapting agriculture to climate change and air pollution".

An international workshop on "Adapting Agriculture to Climate Change and Air Pollution" took place at Nanjing University of Information Science and Technology, Nanjing, China, during 23-27 October, 2023. Experts working in various multi-disciplinary areas of agroecosystem and environmental research gathered for academic communication and discussions. Two discussion groups focused on "agriculture under air pollution and climate change: current challenges and priorities for the future" and "adapting agriculture to air pollution and climate change: current status and next steps." Insights derived from the discussions are summarized in this article and include opinions about current issues, knowledge gaps' identification, and potential priorities and actions that could be taken. The first discussion mainly addresses ozone impact estimates; ozone metrics for impact and risk assessments; ozone monitoring; air pollution impacts and policy; and thepivotal role of agriculture and consumer choices. The second discussion covers adaptation and mitigation; greenhouse gases and energy efficiency; concerns about the link between adaptation and mitigation; local food, planetary-health diets and carbon footprint; irrigation and climate change adaptation; scientific evidence and policy-making; air pollution and crop adaptation; machine learning and crop modeling; and challenges faced by smallholder farmers and large-scale enterprises. Hence, this report could be useful for reseach, educational, and policy purposes, collating opinions of experts working in diverse research areas.

Fair voting outcomes with impact and novelty compromises? Unravelling biases in electing participatory budgeting winners.

Participatory budgeting, as a paradigm for democratic innovations, engages citizens in the distribution of a public budget to projects, which they propose and vote for implementation. So far, voting algorithms have been proposed and studied in social choice literature to elect projects that are popular, while others prioritize a proportional representation of voters' preferences, for instance, the rule of equal shares. However, the anticipated impact and novelty in the broader society by the winning projects, as selected by different algorithms, remains totally under-explored, lacking both a universal theory of impact for voting and a rigorous unifying framework for impact and novelty assessments. This article tackles this grand challenge towards new axiomatic foundations for designing effective and fair voting methods. This is through new and striking insights derived from a large-scale analysis of biases over 345 real-world voting outcomes, characterized for the first time by a novel portfolio of impact and novelty metrics. We find strong causal evidence that equal shares comes with impact loss in several infrastructural projects of different cost levels that have been so far over-represented. However, it also comes with a novel, yet over-represented, impact gain in welfare, education and culture. We discuss the broader implications of these results and how impact loss can be mitigated at the stage of campaign design and project ideation.This article is part of the theme issue 'Co-creating the future: participatory cities and digital governance'.

Quantitatively assessing the impact of the quality of SNOMED CT subtype hierarchy on cohort queries.

SNOMED CT provides a standardized terminology for clinical concepts, allowing cohort queries over heterogeneous clinical data including Electronic Health Records (EHRs). While it is intuitive that missing and inaccurate subtype (or is-a) relations in SNOMED CT reduce the recall and precision of cohort queries, the extent of these impacts has not been formally assessed. This study fills this gap by developing quantitative metrics to measure these impacts and performing statistical analysis on their significance. We used the Optum de-identified COVID-19 Electronic Health Record dataset. We defined micro-averaged and macro-averaged recall and precision metrics to assess the impact of missing and inaccurate is-a relations on cohort queries. Both practical and simulated analyses were performed. Practical analyses involved 407 missing and 48 inaccurate is-a relations confirmed by domain experts, with statistical testing using Wilcoxon signed-rank tests. Simulated analyses used two random sets of 400 is-a relations to simulate missing and inaccurate is-a relations. Wilcoxon signed-rank tests from both practical and simulated analyses (P-values < .001) showed that missing is-a relations significantly reduced the micro- and macro-averaged recall, and inaccurate is-a relations significantly reduced the micro- and macro-averaged precision. The introduced impact metrics can assist SNOMED CT maintainers in prioritizing critical hierarchical defects for quality enhancement. These metrics are generally applicable for assessing the quality impact of a terminology's subtype hierarchy on its cohort query applications. Our results indicate a significant impact of missing and inaccurate is-a relations in SNOMED CT on the recall and precision of cohort queries. Our work highlights the importance of high-quality terminology hierarchy for cohort queries over EHR data and provides valuable insights for prioritizing quality improvements of SNOMED CT's hierarchy.

Enhancing emerging technology discovery in nanomedicine by integrating innovative sentences using BERT and NLDA

Abstract Purpose Nanomedicine has significant potential to revolutionize biomedicine and healthcare through innovations in diagnostics, therapeutics, and regenerative medicine. This study aims to develop a novel framework that integrates advanced natural language processing, noise-free topic modeling, and multidimensional bibliometrics to systematically identify emerging nanomedicine technology topics from scientific literature. Design/methodology/approach The framework involves collecting full-text articles from PubMed Central and nanomedicine-related metrics from the Web of Science for the period 2013–2023. A fine-tuned BERT model is employed to extract key informative sentences. Noiseless Latent Dirichlet Allocation (NLDA) is applied to model interpretable topics from the cleaned corpus. Additionally, we develop and apply metrics for novelty, innovation, growth, impact, and intensity to quantify the emergence of novel technological topics. Findings By applying this methodology to nanomedical publications, we identify an increasing emphasis on research aligned with global health priorities, particularly inflammation and biomaterial interactions in disease research. This methodology provides deeper insights through full-text analysis and leading to a more robust discovery of emerging technologies. Research limitations One limitation of this study is its reliance on the existing scientific literature, which may introduce publication biases and language constraints. Additionally, manual annotation of the dataset, while thorough, is subject to subjectivity and can be time-consuming. Future research could address these limitations by incorporating more diverse data sources, and automating the annotation process. Practical implications The methodology presented can be adapted to explore emerging technologies in other scientific domains. It allows for tailored assessment criteria based on specific contexts and objectives, enabling more precise analysis and decision-making in various fields. Originality/value This study offers a comprehensive framework for identifying emerging technologies in nanomedicine, combining theoretical insights and practical applications. Its potential for adaptation across scientific disciplines enhances its value for future research and decision-making in technology discovery.

Novel R package to quantify and monetize burden of disease attributable to air pollution and noise

Abstract Background Ambient air pollution and noise substantially impact public health and the economy. Different methods to quantify these impacts are described in the literature. Objectives Within the European project BEST-COST (Burden of disease-based methods for estimating the socio-economic cost of environmental stressors), we aim to develop an open-source R package to quantify and monetise the disease burden attributable to air pollution and noise, including the impact of social inequalities. Methods The developed code will allow for different calculation pathways including: single baseline health data vs. age-specific estimates from life tables; relative and absolute risk; scenario comparison based on the population attributable fraction vs. population impact fraction; single-pollutant vs. correlated exposures; and outdoor vs. indoor air pollution. Moreover, the package can digest a variety of input data formats for the exposure-response function (e.g. relative risk with fixed shape vs. user-defined function) and exposure data (e.g. population-weighted mean vs. categorical distribution, spatial data). Eligible impact metrics will include morbidity incidence and prevalence, number of deaths, years of life lost, and years lived with disability. Finally, the R package will enable quantifying social inequities (using a novel multiple deprivation index) and monetising the attributable disease burden (using a state-of-the-art approach). Results During development, the R package will be tested in BEST-COST case studies in 5 European countries (BE, EE, FR, NO, PT), compared with existing tools, and discussed with key users in a workshop. The final R package will be available on GitHub in 2026. Conclusions This tool will allow the quantification and monetisation of the health impact of air pollution and noise. The R package will be a flexible programming resource that can be used as a standalone or combined with existing tools to build a more solid basis for evidence-based policy making. Key messages • This open-source tool will allow the quantification and monetisation of the health impact of air pollution and noise, including the impact of social inequalities. • The R package will be a flexible programming resource that can be used as a standalone or combined with existing tools to build a more solid basis for evidence-based policy making.

Open Access Publishing Metrics, Cost, and Impact in Health Professions Education Journals

Publishing in health professions education (HPE) journals is an integral component of academic discourse and career progression. Research in this field is shifting to an open access (OA) publishing model. To identify the characteristics and publishing models of HPE journals and explore potential associations between publication costs and journal metrics. This cross-sectional study was conducted between September 20, 2023, and February 14, 2024, using the World Bank purchasing power parity (PPP) index to analyze relative costs of article processing charges (APCs). Data on journal characteristics, impact metrics, APCs, and waiver or discount were extracted from the National Library of Medicine, Scimago, Scopus, journal websites, and email correspondence with editorial staff of journals. All HPE journals indexed in PubMed, written in or translated into English, and with HPE as a core component of their mission were included in the analysis. Two-year impact factor, H-index, cite score, Scientific Journal Ranking, and APC. Among the 51 journals included, 27 (53%) adopted OA-only and 24 (47%) adopted hybrid publishing models. The median (IQR) APC for all journals was $2820.00 ($928.00-$3300.00). Associations were observed between impact factor and APC (β coefficient, $386.84; 95% CI, $226.84-$546.84; P < .001) and between cite score and APC (β coefficient, $282.40; 95% CI, $148.12-$416.61; P < .001). Of 20 journal websites with information regarding fee waivers or discounts, 7 journals (35%) confirmed fee waiver or discount. The PPP index analysis of the top 39 countries publishing HPE research showed that the financial burden of meeting the median APC for publication was 1.94 to 10.26 times higher for authors from lower-income countries than for authors from the US. Results of this cross-sectional study suggest that adoption by HPE journals of an OA publishing model was high but access to APC waivers or discounts was limited. These factors create barriers to equitable OA practices, necessitating concerted efforts, such as increasing transparency of publishing costs, implementing economic impact analysis, expanding waivers to eligible authors, and applying holistic impact factor scoring.

Serious Games for Education and Training in the Humanitarian Sector: State of the Art

Serious games and game-based learning have seen slow and stymied adoption in the humanitarian sector, despite their general efficacy as training tools. We looked at over 120 games released between 1990 and 2023 that dealt with topics relevant to the humanitarian sector such as disaster risk management, conflict mitigation, human rights and sustainability, and applied the following filters - serious games, games for education and training, and non-commercial games - to obtain a list of 89 serious games that were designed for awareness, education or training in the humanitarian sector. We then noted their accessibility and investigated the following information for each - year of release, developer, format, platforms, context, purpose, target audience, NGOs involved and related research papers - to understand the trends, challenges and future potential of games as a research and pedagogical tool in this field. Of particular interest to us was the subset of 31 training games aimed at professionals, policy makers and stakeholders. We noted concerning trends regarding the accessibility and longevity of games in this field, and tracked recurrent issues facing the development and dissemination of these games, such as the need for a trained facilitator, the difficulty of adapting the content to new settings, a lack of scientific frameworks to guide their design and a lack of long-term impact metrics. However qualitative surveys conducted by the game designers and some third-party researchers indicate that these games remain a valuable addition to traditional forms of education and training and help enliven a difficult topic through experiential learning. We thus propose that involving professional game designers, embracing digital formats, and investing in archiving and long-term impact studies will greatly benefit the development and accessibility of games for humanitarian education and training.

A Marxist Analysis of the Metrification of Academic Labor: Research Impact Metrics and Socially Necessary Labor Time

Research impact metrics have proliferated in the context of the commodification of academia and academic research, being widely applied for the evaluation of the performance of academic workers. The existing literature on this question has so far failed to satisfactorily explain two key questions in relation to research impact metrics, namely, what dimension of academic labor they refer to and measure, and how their deployment in evaluation processes interweaves with the extension of capitalist relations of production in academia. Examination of the social constitution of research impact metrics from a Marxist perspective shows that metrics are indexes of socially necessary labor time, so that they indicate whether a given expenditure of academic labor has resulted in a use-value and has been undertaken according to the prevailing conditions of production and the average degree of intensity and skill. This analysis will also shed light on why research impact metrics have become so prominent in contemporary academia. The reason lies in the private and independent form taken by social labor in the wake of the unfolding of capitalist relations of production in academia. In conditions of private and independent production, the social validation of labor occurs ex post, namely, once that labor has been expended.

Weather, Climate, and Society: A Retrospective between 2009 and 2022 Using Bibliometric and Content Analysis

Abstract This paper aims to analyze the bibliometrics of articles published in Weather, Climate, and Society (WCAS) over the last 14 years (2009–22). We conducted an analysis using the Scopus database and cross-checked the WCAS online journal and obtained 577 document results (article) and then analyzed and visualized using VOSviewer, Sankey diagrams, altmetric, and content analysis. This research was conducted to show publication trends, citation rates, productivity levels, and developments in research topics at WCAS. The study results show that the 577 articles published in Weather, Climate, and Society have become references for 7106 other articles and are spread across 160 publication sources. Based on the most cited articles, five cluster topics are references for many papers, namely, climate change and environmental management policy, weather risk assessment, climate hazard assessment, anthropogenic climate change, and decision support systems. Climatic Change, International Journal of Disaster Risk Reduction, and Sustainability Switzerland are the three publications with the most citations of articles published by WCAS. Research topics related to climate change, decision-making, risk assessment, the United States, societal impacts, social science, communications/decision-making, and vulnerability are the eight most commonly discussed topics in WCAS articles. Significance Statement From 2009 to 2022, 1773 authors published WCAS articles, either individually or collaboratively, across 1350 institutional affiliations in 70 countries. Using Scopus metadata, this study employs bibliometric and content analysis to explore temporal trends, article impact metrics, citation characteristics, and topic shifts in WCAS publications over this period. The findings indicate that analyzing citations, topics, and authors aids in understanding a journal’s interdisciplinary accessibility. Notably, highly cited papers in WCAS do not necessarily score high on altmetrics, suggesting that future impact measures might integrate academic and social media metrics. Prolific authors often collaborate rather than publish solo. Dominant research topics include climate change, decision-making, risk assessment, social impacts, and U.S. issues.

Analysis of Effects on Scientific Impact Indicators Based on Coevolution of Coauthorship and Citation Networks

This study investigates the coevolution of coauthorship and citation networks and their influence on scientific metrics such as the h-index and journal impact factors. Using a preferential attachment mechanism, we developed a model that integrated these networks and validated it with data from the American Physical Society (APS). While the correlations between reference counts, paper lifetime, and team sizes with scientific impact metrics are well-known, our findings demonstrate how these relationships vary depending on specific model parameters. For instance, increasing reference counts or reducing paper lifetime significantly boosts both journal impact factors and h-indexes, while expanding team sizes without adding new authors can artificially inflate h-indexes. These results highlight potential vulnerabilities in commonly used metrics and emphasize the value of modeling and simulation for improving bibliometric evaluations.

What Environmental Metrics Are Used in Scientific Research to Estimate the Impact of Human Diets?

Metrics drive diagnosis, and metrics will also drive our response to the challenge of climate change. Recognising how current scientific research defines and uses metrics of the environmental impact of human diets is essential to understand which foods, food groups, or dietary patterns are associated with a higher environmental impact. This research, aided by artificial intelligence (AI), aimed to search, map, and synthesise current evidence on the commonly used definitions and metrics of the environmental impacts of human diets. We identified 466 studies measuring the environmental impact of diets. Most studies were from North American or European countries (67%), with data mainly from high-income countries (81%). Most studies did not include methods to recall the provenance of the foods consumed. Most (53%) of the studies only used one metric to estimate the environmental impact of human diets, with 82% of the studies using GHGE. Agreement on how the environmental impact of diets is measured and more comprehensive and accurate data on the environmental impact of single foods is essential to better understand what changes in food systems are needed, at a consumer and policy level, to make a well-meaning change towards a more sustainable diet.

43 Approaches for improving nitrogen utilization efficiency and environmental sustainability of pork production systems

Abstract Nitrogen (N) is an essential element for life, but due to its inefficient utilization, N waste and emissions generated have exceeded the planetary boundaries and ability of the Earth to effectively overcome these environmentally detrimental effects. Global food animal production contributes 29 to 34% of total global N emissions, with pork supply chains contributing about 16% of these emissions. Life cycle N use efficiency (NUE) of U.S. pork production is only about 56%, which is similar to broiler (55%) and egg (50%) production, but greater than beef (10%) and milk (31%) production. However, only about 10 to 44% of dietary N is converted into edible pork products. While genetic improvement has increased NUE associated with improved lean growth rate, improvement in litter size has led to an increased proportion of low-birth-weight pigs that have reduced NUE. Although metabolic modifiers including porcine somatotropin, ractopamine, and immunocastration improve NUE, they are not feasible or acceptable to consumers in many countries. Separate sex feeding and reducing market weight can also improve NUE. Ultimately feeding program design and implementation have the greatest potential to improve NUE because feed is associated with 70% of N emissions in pork production systems. Multi-objective feed formulation using Life Cycle Assessment environmental impact data of feed ingredients, precision feed formulation and feeding practices to overcome variability in digestible nutrient content of feed ingredients and nutritional requirements of pigs fed in groups, feed processing to enhance energy and nutrient digestibility, and minimizing feed wastage offer the greatest opportunity to improve NUE and environmental sustainability. There are substantial differences in greenhouse gas emissions and embedded water and land use associated with growing-finishing feeding programs depending on the dietary proportions of corn, soybean meal, distillers dried grains with solubles (DDGS), and crystalline amino acids among U.S. geographic regions. Furthermore, addition of thermally processed supermarket food waste to corn-soybean meal (CSBM) diets can significantly reduce environmental impacts and diet cost without compromising growth performance and carcass composition compared with current commercial feeding programs. Although growing-finishing feeding programs using CSBM diets with minimal crystalline amino acids result in greater N intake, N retained, and urinary N excretion compared with feeding low protein, crystalline amino acid supplemented (LP) diets, and diets containing 30% DDGS, growth performance and carcass composition are optimized and impacts on climate change, marine and freshwater eutrophication, and fossil resource are reduced by feeding CSBM diets compared with feeding LP or DDGS diets. Sourcing and using feed ingredients with reduced environmental impact metrics along with precision diet formulation and feeding practices to ensure “getting the right amount of energy and nutrients in the right feed fed to the right pigs at the right time” can improve NUE and environmental sustainability of pork production systems.

Integration of Sustainable and Net-Zero Concepts in Shape-Memory Polymer Composites to Enhance Environmental Performance.

This review research aims to enhance the sustainability and functionality of shape-memory polymer composites (SMPCs) by integrating advanced 4D printing technologies and sustainable manufacturing practices. The primary objectives are to reduce environmental impact, improve material efficiency, and expand the design capabilities of SMPCs. The methodology involved incorporating recycled materials, bio-based additives, and smart materials into 4D printing processes, and conducting a comprehensive environmental impact and performance metrics analysis. Significant findings include a 30% reduction in material waste, a 25% decrease in energy consumption during production, and a 20% improvement in shape-memory recovery with a margin of error of ±3%. Notably, the study highlights the potential use of these SMPCs as biomimetic structural biomaterials and scaffolds, particularly in tissue engineering and regenerative medicine. The ability of SMPCs to undergo shape transformations in response to external stimuli makes them ideal for creating dynamic scaffolds that mimic the mechanical properties of natural tissues. This increased design flexibility, enabled by 4D printing, opens new avenues for developing complex, adaptive structures that support cell growth and tissue regeneration. In conclusion, the research demonstrates the potential of combining sustainable practices with 4D printing to achieve significant environmental, performance, and biomedical advancements in SMPC manufacturing.

Designing for Degradation: Transient Devices Enabled by (Nano)Cellulose.

Transient technology involves materials and devices that undergo controlled degradation after a reliable operation period. This groundbreaking strategy offers significant advantages over conventional devices based on non-renewable materials by limiting environmental exposure to potentially hazardous components after disposal, and by increasing material circularity. As the most abundant naturally occurring polymer on Earth, cellulose is an attractive material for this purpose. Besides, (nano)celluloses are inherently biodegradable and have competitive mechanical, optical, thermal, and ionic conductivity properties that can be exploited to develop sustainable devices and avoid the end-of-life issues associated with conventional systems. Despite its potential, few efforts have been made to review current advances in cellulose-based transient technology. Therefore, this review catalogs the state-of-the-art developments in transient devices enabled by cellulosic materials. To provide a wide perspective, the various degradation mechanisms involved in cellulosic transient devices are introduced. The advanced capabilities of transient cellulosic systems in sensing, photonics, energy storage, electronics, and biomedicine are also highlighted. Current bottlenecks toward successful implementation are discussed, with material circularity and environmental impact metrics at the center. It is believed that this review will serve as a valuable resource for the proliferation of cellulose-based transient technology and its implementation into fully integrated, circular, and environmentally sustainable devices.

Scenario Storyline Discovery for Planning in Multi‐Actor Human‐Natural Systems Confronting Change

AbstractScenarios have emerged as valuable tools in managing complex human‐natural systems, but the traditional approach of limiting focus on a small number of predetermined scenarios can inadvertently miss consequential dynamics, extremes, and diverse stakeholder impacts. Exploratory modeling approaches have been developed to address these issues by exploring a wide range of possible futures and identifying those that yield consequential vulnerabilities. However, vulnerabilities are typically identified based on aggregate robustness measures that do not take full advantage of the richness of the underlying dynamics in the large ensembles of model simulations and can make it hard to identify key dynamics and/or storylines that can guide planning or further analyses. This study introduces the FRamework for Narrative Storylines and Impact Classification (FRNSIC; pronounced “forensic”): a scenario discovery framework that addresses these challenges by organizing and investigating consequential scenarios using hierarchical classification of diverse outcomes across actors, sectors, and scales, while also aiding in the selection of scenario storylines, based on system dynamics that drive consequential outcomes. We present an application of this framework to the Upper Colorado River Basin, focusing on decadal droughts and their water scarcity implications for the basin's diverse users and its obligations to downstream states through Lake Powell. We show how FRNSIC can explore alternative sets of impact metrics and drought dynamics and use them to identify drought scenario storylines, that can be used to inform future adaptation planning.

Assessing Indigenous Community Radio as Two-Way Communications Infrastructure: Communal Engagement and Political Mobilization in Ecuador

Because Indigenous peoples face unique challenges to their autonomy and lifeways from dominant media influences, Indigenous radio has been used to facilitate intra-community engagement among these groups. A small but long-standing literature reveals both strengths and vulnerabilities of Indigenous radio, though the rapidly changing communications landscape suggests new possibilities for these media sources. Our research was a community–academic collaboration that employed exploratory and mixed (survey, interview, and observational) methods across two Indigenous communities in the Central and Southern Amazon of Ecuador. The Indigenous radio station, La Voz de la CONFENIAE (Confederación de las Nacionalidades Indígenas de la Amazonía Ecuatoriana), sought to perform an impact assessment that would measure both the character and extent of the impact of radio programming with sensitivity to the priorities of listeners as to the purpose, function, and appropriate impact metrics for an Indigenous radio station. A total of 92 surveys and 30 interviews across two communities were conducted in July and August of 2022. Our findings reveal (a) the informational function of this radio for the Indigenous communities in its listening reach; (b) that radio programming conveys family, community, and cultural knowledge bi-directionally both from and to its listening audience; and (c) that members of the audience were, in turn, prompted to action and engagement. In our discussion, we identify opportunities to improve the assessment of community-owned radio as a bidirectional resource for communities. Our work also advances a model of self-determined and stakeholder-driven evaluation for Indigenous community radio, with particular attention to the material (e.g., behavioral) impacts of radio messages and potential for radio to support communal and collective engagements desired by the communities it serves.

Predicting serious postoperative complications and evaluating racial fairness in machine learning algorithms for metabolic and bariatric surgery

BackgroundPredicting the risk of complications is critical in metabolic and bariatric surgery (MBS). ObjectivesTo develop machine learning (ML) models to predict serious postoperative complications of MBS and evaluate racial fairness of the models. SettingMetabolic and Bariatric Surgery Accreditation and Quality Improvement Program (MBSAQIP) national database, United States. MethodsWe developed logistic regression, random forest (RF), gradient-boosted tree (GBT), and XGBoost model using the MBSAQIP Participant Use Data File from 2016 to 2020. To address the class imbalance, we randomly undersampled the complication-negative class to match the complication-positive class. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC), precision, recall, and F1 score. Fairness across White and non-White patient groups was assessed using equal opportunity difference and disparate impact metrics. ResultsA total of 40,858 patients were included after undersampling the complication-negative class. The XGBoost model was the best-performing model in terms of AUROC; however, the difference was not statistically significant. While the F1 score and precision did not vary significantly across models, the RF exhibited better recall compared to the logistic regression. Surgery type was the most important feature to predict complications, followed by operative time. The logistic regression model had the best fairness metrics for race. ConclusionsThe XGBoost model achieved the highest AUROC, albeit without a statistically significant difference. The RF may be useful when recall is the primary concern. Undersampling of the privileged group may improve the fairness of boosted tree models.