Representation Gap Research Articles

Participation of Older Adults in Clinical Trials for New Drug Applications and Biologics License Applications From 2010 Through 2019

Older age may be accompanied by changes in the pharmacokinetics or pharmacodynamics or both of medications that can result in altered safety and efficacy profiles. To assess representation of older adults in clinical trials of new drug applications (NDAs) and biologics license applications (BLAs). This cross-sectional study analyzed US Food and Drug Administration (FDA) data for NDAs and BLAs approved from 2010 through 2019. Age distribution of clinical trial participants was compared with age distribution of the US population with the disease or disorder (prevalent population). Data were from adults enrolled in registration trials for depression, heart failure, insomnia, non-small cell lung cancer (NSCLC), nonvalvular atrial fibrillation (NVAF) stroke prevention, osteoporosis, and type 2 diabetes or adults sampled from US prevalent population in community-dwelling health data. Data were analyzed from November 2020 to February 2021. Trial enrollment. Representativeness of trial populations was assessed by the participation to prevalence ratio (PPR) defined as the percentage of patients by age group among clinical trial participants to the percentage of patients by age group among US prevalent population. Data from 166 clinical trials (229 558 participants) for 44 NDAs and BLAs were analyzed. The most consistent finding was the limited enrollment of the oldest age groups, namely those 75 years and above for type 2 diabetes and NSCLC, and 80 years and above for NVAF stroke prevention, insomnia, heart failure, and osteoporosis. Adults aged 60 to 74 years were enrolled in equal or greater proportion than the US prevalent population. In this cross-sectional study, underrepresentation of the oldest adults existed during evaluation of new drugs and biologics, yet the older adults may represent significant proportions of the treatment population. Closing the representation gap between clinical trial enrollment and potential treatment populations is essential for safe and effective use of new drugs and biologics.

Challenges in Sub-Kilometer Grid Modeling of the Convective Planetary Boundary Layer

At multi-kilometer grid scales, numerical weather prediction models represent surface-based convective eddies as a completely sub-grid one-dimensional vertical mixing and transport process. At tens of meters grid scales, large-eddy simulation models, explicitly resolve all the primary three-dimensional eddies associated with boundary-layer transport from the surface and entrainment at the top. Between these scales, at hundreds of meters grid size, is a so-called grey zone in which the primary transport is neither entirely sub-grid nor resolved, where explicit large-eddy models and sub-grid boundary-layer parameterization models fail in different ways that are outlined in this review article. This article also reviews various approaches that have been taken to span this gap in the proper representation of eddy transports in the sub-kilometer grid range using scale-aware approaches. Introduction of moisture with condensation in the eddies expands this problem to that of handling shallow convection, but similarities between dry and cloud-topped convective boundary layers can lead to some unified views of the processes that need to be represented in convective boundary-layers which will be briefly addressed here.

The Voice of the Absent? The Link Between Descriptive and Substantive Representation of the Working Class in Western Europe

Recent research has revealed a considerable representation gap disadvantaging the lower social class in the political process. However, we know little about the underlying mechanisms of this bias or the measures that could compensate for it. Combining cross-national data from a general population survey and an elite-level survey, the present article addresses this knowledge deficit by looking at one potential determinant of working-class underrepresentation: the unequal composition of parliaments. Building on arguments for descriptive representation, I argue that members of the working class experience similar living situations and life chances that form their preferences. Consequently, working-class politicians may be better suited to representing working-class views. The results confirm lower congruence levels between the political elite and working-class citizens. However, class-based preference gaps among politicians are relatively small, and politicians’ social class appears to have a limited impact on compensating for the representational inequality of the working class.

How granularity of data matters in understanding and accelerating racial diversity in U.S. clinical trials.

88 Background: Historically, US clinical trials have been shown to recruit disproportionately large percentages of White patients, raising concerns about the generalizability of clinical trial results to underrepresented racial minority patient populations. Because of this, the FDA has recently reiterated its guidance stating that clinical trials need to reflect the demographic distribution of the US. In this research, we sought to understand how participation of Black patients varies by therapeutic area and geography within the US. Methods: Studying patient-level clinical trial data from an industry-leading historical clinical trial data repository of over 8 million patients from 27,000 clinical trials, we assessed the racial composition of US interventional trials across indications from 2010 to 2021 which encompassed 433,822 clinical trial participants across 2,997 trials. We also analyzed participants’ racial composition within the subset of trials from three distinct therapeutic areas (i.e., oncology n = 118,194, cardiovascular n = 12,281, central nervous system n = 35,533) and distinct sites over the same period. Results: The racial distribution of clinical trial participants in the US across all therapeutic areas was 78% White, 15% Black, and 3% Asian. Within the three distinct therapeutic areas, the proportion of Black clinical trial participants varied, with oncology trials reporting 8.5% Black participants, cardiovascular trials reporting 15.3% Black participants, and central nervous system trials reporting 19.9% Black participants. Black participation also varied within a therapeutic area, depending on the indication, as well as by site, with individual sites contributing differently to the racial diversity of trials. Conclusions: Our analysis shows that US interventional trials enroll 15% Black participants, appearing consistent with the 2020 US Census, which estimates that 14.6% of Americans are Black. However, more granular analyses at the level of therapeutic area, indication and site suggest substantial variation in Black participation in clinical trials including a gap in Black representation in oncology trials vs. US Census estimates. This research suggests that aggregate estimates of racial enrollment may mask dramatic variation by other factors like granularity of disease area and geographic location. The study of historic clinical trial data may yield useful insights for accelerating diverse representation in clinical trials.

Publicize or Perish—Challenger party success through megaphones and locomotives

AbstractWhat makes some challenger parties succeed and others fail? Existing research on party‐level factors finds that it is essential for parties to close a representational gap. However, this condition cannot be sufficient. For each successful challenger, there are many others proclaiming a similar message but going unheard. Hence, we argue that, instead of only to the messages, more attention should be paid to parties’ abilities to communicate their messages effectively.Using an original dataset on 74 challenger parties in five countries in a similar political and economic situation (during the post‐2008 economic crisis), we show that communication is key for electoral success. In particular, we show that challenger parties can win over voters by, on the one hand, harnessing the prominence of a well‐known personality (a locomotive) and by, on the other hand, establishing a means of contacting voters which bypasses the traditional news media and amplifies their message (like a megaphone). But this megaphone only works if it amplifies a message that fills a representational gap (here: an anti‐austerity message) – only then do parties benefit. Furthermore, we provide evidence for the widespread but unproven claim that populism helps challenger parties succeed, but this, too, depends on whether parties are able to contact voters on a large scale.By including three crucial aspects of communication (sender, channel, and message), we can explain a large share of the high variability in challenger party success.

Representation and diversity in the sign language translation and interpreting profession in the United Kingdom

This article reports the findings of a nationwide sign language translator and interpreter (SLTI) census to establish a baseline description of the United Kingdom’s SLTI workforce that was commissioned by the Association of Sign Language Interpreters UK. Complete responses were received from 690 practitioners from across the United Kingdom (43% of the potential sample). The survey responses were analysed using the SPSS statistical software, specifically to look at various intersectional characteristics concerning gender, age, sexuality, race, and ethnicity. This article provides an overview of the findings with respect to two key themes: representation and diversity in the profession, along with discussion of the profile of the SLTI profession in the United Kingdom, and recommendations for actions for key stakeholder organisations. As a first snapshot, this census functions as a baseline for future comparisons, and can be modified and improved through open dialogue with professional and community groups. This snapshot helps us to identify gaps in representation. Finally, it can also inform the planning and forecasting of recruitment needs for the workforce and highlight any education and training needs.

34986 Optimizing representation of skin of color in preclinical medical curricula at two Mayo Medical School campuses

By 2045, the US Census Bureau projects that the majority of the US population will be people of color. This study aims to provide data and a framework to evaluate and ensure equitable representation of skin of color within medical curricula images. The Mayo Clinic Alix School of Medicine Diversity, Equity, and Inclusion Curriculum Review Committee investigated the representation of skin of color in the preclinical medical curriculum on the Minnesota and Arizona campuses, evaluating all illustrations or photographs of human subjects. 2193 images in Minnesota and 805 images in Arizona were categorized based on a Fitzpatrick skin type scale graphic and compared with the national distribution using a chi-square goodness of fit. A skin type graphic was appropriate as the study purpose was to evaluate lightness/darkness of depicted skin rather than photosensitivity. Images used in the Minnesota χ2(5,12) = 1902307, P < .0001 and Arizona χ2(5, 12) = 112534, P < .0001 curricula were significantly different from the national distribution. The largest discrepancy on both campuses was the overrepresentation of Fitzpatrick type I images in preclinical curriculum. Fitzpatrick types III, VI, and IV were underrepresented at both campuses in order of descending magnitude. Postreview meetings with course directors critically examined nuance, implicit bias, and stereotype imprinting, developing attainable, high-yield interventions. The knowledge gap in dermatology education and disproportionate representation of lighter skin in teaching materials has been recently documented in the literature. To ensure accurate diagnosis and high-quality care for diverse patient populations, human imagery within medical curricula and reference material should, at a minimum, reflect population demographics.

Levers of change: using mathematical models to compare gender equity interventions in universities.

Women are under-represented in academic staff in universities worldwide. Our work builds on other studies of ‘demographic inertia'. We find that time will not bridge the gender representation gap in academia, and echo others in saying bold actions are required to reach parity. Our work then uses New Zealand's unique system of scoring individual research performance to test empirically which levers universities should pull, and in which combinations. We combine individual research performance scores with 20 years of data from one university to parametrize a rank-structured mathematical model using Leslie matrices. Our model compares three key levers of change at universities' disposal—hiring, promotion and attrition. We apply the model to a bifurcated population of university staff—those with high research activity, and those who are moderately active—based on their national research quality score. We then test levers in various combinations that management could pull to improve gender representation. We find that the solutions are different for the high versus moderate research performers. For individuals with high research activity, universities should concentrate on equitable hiring practices. For those with more moderate research activity, more equitable promotion practices hold the key.

Towards prior gap and representation gap for long-tailed recognition

Most deep learning models are elaborately designed for balanced datasets, and thus they inevitably suffer performance degradation in practical long-tailed recognition tasks, especially to the minority classes. There are two crucial issues in learning from imbalanced datasets: skew decision boundary and unrepresentative feature space. In this work, we establish a theoretical framework to analyze the sources of these two issues from Bayesian perspective, and find that they are closely related to the prior gap and the representation gap, respectively. Under this framework, we show that existing long-tailed recognition methods manage to remove either the prior gap or the presentation gap. Different from these methods, we propose to simultaneously remove the two gaps to achieve more accurate long-tailed recognition. Specifically, we propose the prior calibration strategy to remove the prior gap and introduce three strategies (representative feature extraction, optimization strategy adjustment and effective sample modeling) to mitigate the representation gap. Extensive experiments on five benchmark datasets validate the superiority of our method against the state-of-the-art competitors.

Nascent narratives of Armenian remembrance: The Armenian genocide reflected in the Armenian-American press

This article explores the cultural memory of the Armenian genocide archived, to a major extent, in non-digitized form. In the initial decades following the genocide, the memory of the crimes committed against Armenians in 1915 was almost non-existent in the public space of America. Monuments, demonstrations, state, and international resolutions, and other instruments of memorialization did not materialize until the 1960s when, as a result of worldwide Armenian mobilization ahead of the 50th anniversary, traces of genocide remembrance were gradually brought to life. Analyzing two Armenian newspapers from the United States – Hairenik Weekly (HW) and The Armenian Mirror-Spectator (AMS) – this paper reveals how Armenians recollected the genocide in the decades preceding the emergence of subsequent lieux de mémoire. What evoked their memories before 1965? And how did narratives change over time, eventually leading to the “exteriorization of Armenian memory”? The case of the Armenian genocide shows that memories of a traumatic event can quickly penetrate the cultural sphere, but remain closed for longer in the narrow framework of a specific community. This had consequences, including an almost complete lack of representation of the genocide in the public domain – one that would be designed by Armenians for non-Armenians. The process of meaning-making (traced through editorials from the two Armenian-American newspapers) influenced a gradual bridging of the representation gap in the American public space, beginning in 1965.

COVID-19 burden, author affiliation and women's well-being: A bibliometric analysis of COVID-19 related publications including focus on low- and middle-income countries.

SummaryBackgroundPublished literature documents tremendous gender inequities in the social, economic and health effects of the COVID-19 pandemic, but less evidence has come from low- and middle-income countries (LMICs) and even less from LMIC-based authors. We examine whether a) COVID-19 burden and b) LMIC-based authorship were associated with academic publications related to COVID-19 and women's well-being in LMICs.MethodsWe reviewed academic articles on COVID-19 and women's well-being in LMICs published between February 2020 and May 2021 (n=1076 articles), using six electronic databases (PubMed, Web of Science, PsycInfo, EconLit, RePeC, NBER). Multilevel, mixed effects linear regressions assessed the relationships between each of our independent variables - a) COVID-19 burden (cases/100 population, deaths/100 population, deaths/cases) and b) author's country of primary affiliation, with publications related to COVID-19 and women's well-being, both overall and stratified by country income group.FindingsEight-eight percent of articles had lead and/or senior authors affiliated with in-country institutions. Linear mixed effect models indicate that COVID-19 cases and case fatality ratios in a country were significantly and positively associated with the number of publications related to COVID-19 and women's well-being, though these relationships were significant only in upper-middle income group countries in stratified analyses. LMIC lead and senior authorship were also significantly and positively associated with our outcome, after adjusting for COVID-19 burden.InterpretationWhile the majority of COVID-19 research examining women's well-being in LMICs in the first year and a half of the pandemic included country-affiliated author leadership, there were important gaps in representation. Findings highlight the importance of LMIC-based scholars to build local and gendered research in crises.FundingBill and Melinda Gates Foundation (INV-018007).

Distribution alignment for cross-device palmprint recognition

With the development of IoT and mobile devices, cross-device palmprint recognition is becoming an emerging research topic in multimedia for its great application potential. Due to the diverse characteristics of different devices, e.g.resolution or artifacts caused by post-processing, cross-device palmprint recognition remains a challenging problem. In this paper, we make efforts to improve cross-device palmprint recognition in two aspects: (1) we put forward a novel distribution-based loss to narrow the representation gap across devices, and (2) we establish a new cross-device benchmark based on existing palmprint recognition datasets. Different from many recent studies that only utilize instance-level or pairwise-level information between devices, the proposed progressive target distribution loss (PTD loss) uses the distributional information. Moreover, we establish a progressive target mechanism that will be dynamically updated during training, making the optimization easier and smoother. The newly established benchmark contains more samples and more types of IoT devices than previous benchmarks, which can facilitate cross-device palmprint research. Extensive comparisons on several benchmarks reveal that: (1) our method outperforms other cross-device biometric recognition approaches significantly; (2) our method presents superior performance compared to SOTA competitors on several general palmprint recognition benchmarks; Code and data are openly available at https://kaizhao.net/palmprint.

Nevertheless, They Persisted: Factors that Promote Persistence for Women and Racially/Ethnically Minoritized Students in Undergraduate Computing

ABSTRACT Background and Context As computing fields aim to both expand and diversify, narrowing representation gaps in undergraduate computing majors requires focus on retaining women and racially/ethnically minoritized students to the point of degree attainment. Objective This study addresses the factors that contribute to persistence in computing majors among undergraduate students who took introductory computing courses during the first two years of college. Method Student survey data from 15 research universities in the United States were used to explore differences in persistence patterns by students’ gender and racial/ethnic identities. Further, we used logistic regression to examine factors that promote persistence in computing majors, with attention to conditional effects by gender and race/ethnicity. Findings Results show that women are less likely than men to persist in computing majors two years following completion of the introductory CS course. Findings suggest that proximal socialization experiences, specifically those related to students’ self-confidence, sense of fit, and in-class experiences, are important to student persistence in computing fields. Implications: The results suggest that peer experiences in computing are central to student persistence in the major. Hence, computing departments can act on these findings by strengthening the community within their majors and fostering positive peer interactions among students.

Diversifying Representations of Female Scientists on Social Media: A Case Study From the Women Doing Science Instagram

In the absence of real-life role models, women scientists portrayed in the media enable young women to imagine themselves as future scientists. Both traditional media and social media have the potential to provide role models, but their representations of scientists reinforce, rather than challenge, long-standing gendered stereotypes. Women Doing Science, a social media effort, was founded by the authors to address this representation gap by sharing daily photos of diverse women in science, technology, engineering, and math (STEM) with accompanying research descriptions in English and in other languages. To date, Women Doing Science has highlighted over 800 scientists to an audience of ~100,000 followers from around the world, who are primarily women in undergraduate and graduate STEM degree programs. Here, we evaluate the success of the Women Doing Science Instagram page in portraying women scientists with diverse racial and national identities. Furthermore, we explore which aspects of posts drive higher engagement from the audience. We find that our Instagram audience has higher engagement with posts featuring Women of Color, multiple languages, and posts that challenge stereotypes associated with women in STEM. In addition, we find that Women of Color are more likely to include additional aspects of their identity in their biographies, and that a primary reason our audience follows the page is because of the diversity portrayed in the posts. These results imply the powerful potential for social media platforms like Instagram to source diverse role models that expand conventional images of STEM professionals and allow international audiences to develop their STEM identities.

Understanding the disparity in n-alkane production among angiosperms and gymnosperms from the higher Himalayas: Inferences drawn from a Machine Learning approach

The relative abundances of n-alkanes in plant-derived organic matter serve as reliable biomarkers in paleoecological studies, given that leaf wax characteristics in terrestrial plants are a function of the external environment. Owing to limited phylogenetic and geographic sampling in earlier studies, the relative weight of environmental factors and plant phylogeny on leaf wax production is yet to be well constrained. Here, we try to address the gap in the geographical representation of terrestrial plants and explain their inherent disparity in n-alkane abundance as a function of environment and phylogeny. To do so, we examined 13 plant species, ranging from 500 to 5200 m in altitude, from the northwest to eastern Himalayas, covering three countries, contrasting climate, diverse forest types, over three years. We report: (a) Juniperus (gymnosperm) produces twice the amount of n-alkanes compared to the coexisting angiosperms; (b) gymnosperm n-alkane concentration is seven times higher above the snowline (3000 m) than below due to a paucity of moisture and stronger solar influx; (c) angiosperms n-alkane abundance mimics the precipitation bands in the Himalayas at 2000 m and 4000 m altitude where the n-alkane concentration is three times higher compared to other areas; and (d) the response of plant physiology to climatic forcings is observed in Rhododendron where n-alkane production is connected to the timing of flowering and seed-ripening. We applied a Machine Learning classifier built on Principal Component Analysis and Logistic Regression to disentangle a plant's phylogenetic signal from its external environment, which suggests a 30% higher weight of phylogeny on n-alkane production than the external environment.

Analysis of the Role of Women in Gender Insight Political Development: Documentation Study on Gender in Indonesia

Gender equality and women's representation in Indonesia's political development are crucial issues that have become endless problems for Indonesia. The gap in representation between women and men is seen in the low percentage of women political participants who occupy positions in the DPR RI. The purpose of this study is to examine and analyze the role of women in political development in Indonesia and to determine the factors that influence the low involvement of women in the political sphere. This study uses a qualitative method based on the philosophy of positivism with a documentation study approach that obtains sources of information through texts, manuscripts, and previous research. Based on the results of documentation and literature studies, it is known that women's participation in Indonesia is still quite low even though the government has issued a law that regulates women's representation in political parties. The results show that the problem of the gap in the participation of women and men in political development in Indonesia is caused by the feminine factors inherent in women, in order to overcome this, women need to improve their quality so that they can participate more in political development in Indonesia.

Validation studies of rheumatoid arthritis patient-reported outcome measures in populations at risk for inequity: A systematic review and analysis using the OMERACT summary of measurement properties equity table

BackgroundExisting patient-reported outcome measures (PROMs) in rheumatoid arthritis (RA) may be limited in their applicability to populations that are at risk for inequities. We conducted a systematic review to identify and rate evidence in the validation studies for PROMs in populations at risk for inequity. MethodsA systematic review of MEDLINE and EMBASE was completed. The search strategy was developed to identify measurement property studies for PROMs of interest (selected pain, disease activity, global evaluation and quality of life scales) in patients with RA. We identified experimental, observational, and qualitative studies reporting analysis of feasibility, construct validity and discriminant ability metrics for populations at risk for inequity by various factors including race, ethnicity, culture or language; employment status; sex and gender identity; education level; socioeconomic status; social support; age; health literacy and disability. These were rated based on the OMERACT Summary of Measurement Properties Equity table. ResultsFrom 19,786 titles and abstracts screened, we identified 14 unique studies reporting validation metrics for pain (n = 3), DAS28-ESR or DAS28-CRP (n = 2), ACR20 (n = 1), patient global assessment (n = 2), EQ5D (n = 4), and PROMIS® (n = 3) by race (n = 10 studies), age (n = 6 studies), sex (n = 5 studies), education level (n = 2 studies), and disability, literacy, employment status, social support level and socioeconomic status (n = 1 study each). Five studies reported on feasibility, 12 reported construct validity metrics, and 4 studies reported on discriminant validity metrics. All studies by culture or language were rated as having good measurement property metrics. There was limited assessment of measurement property metrics for other populations at risk for inequity. ConclusionOur study highlights important gaps in patient representation in rheumatology research for accepted outcome measures. New outcome measures being developed for research purposes and clinical practice should ensure and report representation of patients from populations at risk for inequities in the testing of metrics of feasibility, construct validity and discriminant ability metrics.

A reflection of life - my tale of microdiversity, equity and inclusion in cell biology (and beyond).

A reflection of life - my tale of microdiversity, equity and inclusion in cell biology (and beyond).

“Don’t they jump on the seats?” The underrepresentation of migrant and minority artists in the cultural labour market of Brussels

ABSTRACT While migration has made cities more super-diverse than ever, artists with migrant backgrounds are underrepresented in the creative and cultural industries. Although existing literature pays attention to minorities’ unequal labour market access, the question of how the careers and working conditions of artists with a migrant background play out in local cultural labour markets remains underexplored. Based on the case of the performing arts sector in Brussels, I outline the factors that explain the gap in cultural labour market representation between ethnic minorities and the white majority population. I contend that minority artists have more difficulties in entering the cultural labour market than native artists because they face structural and personal hurdles throughout their careers. Furthermore, I show that minority artists develop various strategies to overcome these barriers. This paper uncovers the mechanisms that contribute to the emergence and persistence of this “creative mismatch” in local cultural labour markets.

Generative Adversarial Networks for Unmanned Aerial Vehicle Object Detection with Fusion Technology

Unmanned aerial vehicles (UAVs) also called as a drone comprises of a controller from the base station along with a communications system with the UAV. The UAV plane can be precisely controlled by a machine operator, similar to remotely directed aircraft, or with increasing grades of autonomy, as like autopilot assistance, up to completely self-directed aircraft that require no human input. Obstacle detection and avoidance is important for UAVs, particularly lightweight micro aerial vehicles, but it is a difficult problem to solve because pay load restrictions limit the number of sensors that can be mounted onto the vehicle. Lidar uses Laser for finding the distance between objects and vehicle. The speed and direction of the moving objects are detected and tracked with the help of radar. When many sensors are deployed, both thermal and electro-optro cameras have great clustering capabilities as well as accurate localization and ranging. The purpose of the proposed architecture is to create a fusion system that is cost-effective, lightweight, modular, and robust as well. Also, for tiny object detection, we recommend a novel Perceptual Generative Adversarial Network method that bridges the representation gap between small and large objects. It employs the Generative Adversarial Networks (GAN) algorithm, which iimproves object detection accuracy above benchmark models at the same time maintaining real-time efficiency in an embedded computer for UAVs. Its generator, in particular, learns to turn unsatisfactory tiny object representations into super-resolved items that are similar to large objects to deceive a rival discriminator. At the same time, its discriminator contests with the generator to classify the engendered representation, imposing a perceptual restriction on the generator: created representations of tiny objects must be helpful for detection. With three different obstacles, we were able to successfully identify and determine the magnitude of the barriers in the first trial. The accuracy of proposed models is 83.65% and recall is 81% which is higher than the existing models.