scispace - formally typeset
Search or ask a question
Author

Jordan D. Dworkin

Bio: Jordan D. Dworkin is an academic researcher from Columbia University. The author has contributed to research in topics: Medicine & Psychology. The author has an hindex of 12, co-authored 29 publications receiving 470 citations. Previous affiliations of Jordan D. Dworkin include Columbia University Medical Center & University of Pennsylvania.

Papers
More filters
Journal ArticleDOI
TL;DR: It is found that women-led work tends to be undercited relative to expectations and this imbalance is driven largely by the citation practices of men and is increasing over time as the field diversifies.
Abstract: Similarly to many scientific disciplines, neuroscience has increasingly attempted to confront pervasive gender imbalances. Although publishing and conference participation are often highlighted, recent research has called attention to the prevalence of gender imbalance in citations. Because of the downstream effects of citations on visibility and career advancement, understanding the role of gender in citation practices is vital for addressing scientific inequity. Here, we investigate whether gendered patterns are present in neuroscience citations. Using data from five top neuroscience journals, we find that reference lists tend to include more papers with men as first and last author than would be expected if gender were unrelated to referencing. Importantly, we show that this imbalance is driven largely by the citation practices of men and is increasing over time as the field diversifies. We assess and discuss possible mechanisms and consider how researchers might approach these issues in their own work.

299 citations

Posted ContentDOI
11 Jan 2020-bioRxiv
TL;DR: It is found that reference lists tend to include more papers with men as first and last author than would be expected if gender were not a factor in referencing, and this overcitation of men and undercitation of women is driven largely by the citation practices of men, and is increasing over time as the field becomes more diverse.
Abstract: Like many scientific disciplines, neuroscience has increasingly attempted to confront pervasive gender imbalances within the field. While much of the conversation has centered around publishing and conference participation, recent research in other fields has called attention to the prevalence of gender bias in citation practices. Because of the downstream effects that citations can have on visibility and career advancement, understanding and eliminating gender bias in citation practices is vital for addressing inequity in a scientific community. In this study, we sought to determine whether there is evidence of gender bias in the citation practices of neuroscientists. Using data from five top neuroscience journals, we find that reference lists tend to include more papers with men as first and last author than would be expected if gender were not a factor in referencing. Importantly, we show that this overcitation of men and undercitation of women is driven largely by the citation practices of men, and is increasing over time as the field becomes more diverse. We develop a co-authorship network to assess homophily in researchers9 social networks, and we find that men tend to overcite men even when their social networks are representative. We discuss possible mechanisms and consider how individual researchers might address these findings in their own practices.

177 citations

Posted ContentDOI
12 Oct 2020-bioRxiv
TL;DR: It is shown that reference lists tend to include more papers with a White person as first and last author than would be expected if race and ethnicity were unrelated to referencing, and this imbalance is driven largely by the citation practices of White authors, and is increasing over time even as the field diversifies.
Abstract: Discrimination against racial and ethnic minority groups exists in the academy, and the associated biases impact hiring and promotion, publication rates, grant funding, and awards. Precisely how racial and ethnic bias impacts the manner in which the scientific community engages with the ideas of academics in minority groups has yet to be fully elucidated. Citations are a marker of such community engagement, as well as a currency used to attain career milestones. Here we assess the extent and drivers of racial and ethnic imbalance in the reference lists of papers published in five top neuroscience journals over the last 25 years. We find that reference lists tend to include more papers with a White person as first and last author than would be expected if race and ethnicity were unrelated to referencing. We show that this imbalance is driven largely by the citation practices of White authors, and is increasing over time even as the field diversifies. To further explain our findings, we examine co-authorship networks and find that while the network has become markedly more integrated in general, the current degree of segregation by race/ethnicity is greater now than it has been in the past. Citing further from oneself on the network is associated with greater balance, but White authors’ preferential citation of White authors remains even at high levels of network exploration. We also quantify the effects of intersecting identities, determining the relative costs of gender and race/ethnicity, and their combination in women of color. Our findings represent a call to scientists and journal editors of all disciplines to consider the ethics of citation practices, and actions to be taken in support of an equitable future.

102 citations

Journal ArticleDOI
TL;DR: While the COVID-19 pandemic disproportionately impacts marginalized communities, no empiric US-based research has focused specifically on transgender and gender nonbinary (TGNB) individuals as discussed by the authors.
Abstract: While the COVID-19 pandemic in the United States disproportionately impacts marginalized communities, no empiric US-based research has focused specifically on transgender and gender nonbinary (TGNB...

70 citations


Cited by
More filters
Journal ArticleDOI
TL;DR: Both the updated CONSORT extension for NPT trials and the Consolidated Standards of Reporting Trials extension for abstracts should help authors, editors, and peer reviewers improve the transparency of NPT trial reports.
Abstract: Incomplete and inadequate reporting is an avoidable waste that reduces the usefulness of research. The CONSORT (Consolidated Standards of Reporting Trials) Statement is an evidence-based reporting guideline that aims to improve research transparency and reduce waste. In 2008, the CONSORT Group developed an extension to the original statement that addressed methodological issues specific to trials of nonpharmacologic treatments (NPTs), such as surgery, rehabilitation, or psychotherapy. This article describes an update of that extension and presents an extension for reporting abstracts of NPT trials. To develop these materials, the authors reviewed pertinent literature published up to July 2016; surveyed authors of NPT trials; and conducted a consensus meeting with editors, trialists, and methodologists. Changes to the CONSORT Statement extension for NPT trials include wording modifications to improve readers' understanding and the addition of 3 new items. These items address whether and how adherence of participants to interventions is assessed or enhanced, description of attempts to limit bias if blinding is not possible, and specification of the delay between randomization and initiation of the intervention. The CONSORT extension for abstracts of NPT trials includes 2 new items that were not specified in the original CONSORT Statement for abstracts. The first addresses reporting of eligibility criteria for centers where the intervention is performed and for care providers. The second addresses reporting of important changes to the intervention versus what was planned. Both the updated CONSORT extension for NPT trials and the CONSORT extension for NPT trial abstracts should help authors, editors, and peer reviewers improve the transparency of NPT trial reports.

650 citations

Journal ArticleDOI
TL;DR: This study represents the first meta-analysis and systematic review investigating test-retest reliability of edge-level functional connectivity and suggests there is room for improvement, but care should be taken to avoid promoting reliability at the expense of validity.

325 citations

Journal ArticleDOI
01 Jul 2019-Brain
TL;DR: A practical guide to the proper recognition of multiple sclerosis lesions is provided, including a thorough description and illustration of typical MRI features, as well as a discussion of red flags suggestive of alternative diagnoses.
Abstract: MRI has improved the diagnostic work-up of multiple sclerosis, but inappropriate image interpretation and application of MRI diagnostic criteria contribute to misdiagnosis. Some diseases, now recognized as conditions distinct from multiple sclerosis, may satisfy the MRI criteria for multiple sclerosis (e.g. neuromyelitis optica spectrum disorders, Susac syndrome), thus making the diagnosis of multiple sclerosis more challenging, especially if biomarker testing (such as serum anti-AQP4 antibodies) is not informative. Improvements in MRI technology contribute and promise to better define the typical features of multiple sclerosis lesions (e.g. juxtacortical and periventricular location, cortical involvement). Greater understanding of some key aspects of multiple sclerosis pathobiology has allowed the identification of characteristics more specific to multiple sclerosis (e.g. central vein sign, subpial demyelination and lesional rims), which are not included in the current multiple sclerosis diagnostic criteria. In this review, we provide the clinicians and researchers with a practical guide to enhance the proper recognition of multiple sclerosis lesions, including a thorough definition and illustration of typical MRI features, as well as a discussion of red flags suggestive of alternative diagnoses. We also discuss the possible place of emerging qualitative features of lesions which may become important in the near future.

270 citations

Posted Content
TL;DR: This article proposed a Bayesian causal forest model for estimating heterogeneous treatment effects from observational data, which is geared specifically towards situations with small effect sizes, heterogeneous effects, and strong confounding.
Abstract: This paper presents a novel nonlinear regression model for estimating heterogeneous treatment effects from observational data, geared specifically towards situations with small effect sizes, heterogeneous effects, and strong confounding. Standard nonlinear regression models, which may work quite well for prediction, have two notable weaknesses when used to estimate heterogeneous treatment effects. First, they can yield badly biased estimates of treatment effects when fit to data with strong confounding. The Bayesian causal forest model presented in this paper avoids this problem by directly incorporating an estimate of the propensity function in the specification of the response model, implicitly inducing a covariate-dependent prior on the regression function. Second, standard approaches to response surface modeling do not provide adequate control over the strength of regularization over effect heterogeneity. The Bayesian causal forest model permits treatment effect heterogeneity to be regularized separately from the prognostic effect of control variables, making it possible to informatively "shrink to homogeneity". We illustrate these benefits via the reanalysis of an observational study assessing the causal effects of smoking on medical expenditures as well as extensive simulation studies.

179 citations

Journal ArticleDOI
TL;DR: A systematic review of the nature and prevalence of spin in the biomedical literature, which investigated spin in clinical trials, observational studies, diagnostic accuracy studies, systematic reviews, and meta-analyses.
Abstract: In the scientific literature, spin refers to reporting practices that distort the interpretation of results and mislead readers so that results are viewed in a more favourable light. The presence of spin in biomedical research can negatively impact the development of further studies, clinical practice, and health policies. This systematic review aims to explore the nature and prevalence of spin in the biomedical literature. We searched MEDLINE, PreMEDLINE, Embase, Scopus, and hand searched reference lists for all reports that included the measurement of spin in the biomedical literature for at least 1 outcome. Two independent coders extracted data on the characteristics of reports and their included studies and all spin-related outcomes. Results were grouped inductively into themes by spin-related outcome and are presented as a narrative synthesis. We used meta-analyses to analyse the association of spin with industry sponsorship of research. We included 35 reports, which investigated spin in clinical trials, observational studies, diagnostic accuracy studies, systematic reviews, and meta-analyses. The nature of spin varied according to study design. The highest (but also greatest) variability in the prevalence of spin was present in trials. Some of the common practices used to spin results included detracting from statistically nonsignificant results and inappropriately using causal language. Source of funding was hypothesised by a few authors to be a factor associated with spin; however, results were inconclusive, possibly due to the heterogeneity of the included papers. Further research is needed to assess the impact of spin on readers’ decision-making. Editors and peer reviewers should be familiar with the prevalence and manifestations of spin in their area of research in order to ensure accurate interpretation and dissemination of research.

174 citations