Examining Linguistic Differences in Electronic Health Records for Diverse Patients With Diabetes: Natural Language Processing Analysis.

Background: Individuals from minoritized racial and ethnic backgrounds experience pernicious and pervasive health disparities that have emerged, in part, from clinician bias. Objective: We used a natural language processing approach to examine whether linguistic markers in electronic health record (EHR) notes differ based on the race and ethnicity of the patient. To validate this methodological approach, we also assessed the extent to which clinicians perceive linguistic markers to be indicative of bias. Methods: In this cross-sectional study, we extracted EHR notes for patients who were aged 18 years or older; had more than 5 years of diabetes diagnosis codes; and received care between 2006 and 2014 from family physicians, general internists, or endocrinologists practicing in an urban, academic network of clinics. The race and ethnicity of patients were defined as White non-Hispanic, Black non-Hispanic, or Hispanic or Latino. We hypothesized that Sentiment Analysis and Social Cognition Engine (SEANCE) components (ie, negative adjectives, positive adjectives, joy words, fear and disgust words, politics words, respect words, trust verbs, and well-being words) and mean word count would be indicators of bias if racial differences emerged. We performed linear mixed effects analyses to examine the relationship between the outcomes of interest (the SEANCE components and word count) and patient race and ethnicity, controlling for patient age. To validate this approach, we asked clinicians to indicate the extent to which they thought variation in the use of SEANCE language domains for different racial and ethnic groups was reflective of bias in EHR notes. Results: We examined EHR notes (n=12,905) of Black non-Hispanic, White non-Hispanic, and Hispanic or Latino patients (n=1562), who were seen by 281 physicians. A total of 27 clinicians participated in the validation study. In terms of bias, participants rated negative adjectives as 8.63 (SD 2.06), fear and disgust words as 8.11 (SD 2.15), and positive adjectives as 7.93 (SD 2.46) on a scale of 1 to 10, with 10 being extremely indicative of bias. Notes for Black non-Hispanic patients contained significantly more negative adjectives (coefficient 0.07, SE 0.02) and significantly more fear and disgust words (coefficient 0.007, SE 0.002) than those for White non-Hispanic patients. The notes for Hispanic or Latino patients included significantly fewer positive adjectives (coefficient -0.02, SE 0.007), trust verbs (coefficient -0.009, SE 0.004), and joy words (coefficient -0.03, SE 0.01) than those for White non-Hispanic patients. Conclusions: This approach may enable physicians and researchers to identify and mitigate bias in medical interactions, with the goal of reducing health disparities stemming from bias.

How Can Big Data Science Transform the Psychological Sciences?

Big data and related technologies are radically altering our society. In a similar way, these approaches can transform the psychological sciences. The goal of this commentary is to motivate psychologists to embrace big data science for the betterment of the field. Big data sources, algorithmic methods, and a culture that embraces prediction has the potential to advance our science, improve the robustness and replicability of our research, and allow us to focus more centrally on actual behaviors. We highlight these key transformations, acknowledge criticisms of big data approaches, and emphasize specific ways psychologists can contribute to the big data science revolution.

From alpha to omega and beyond! A look at the past, present, and (possible) future of psychometric soundness in the Journal of Applied Psychology.

The psychometric soundness of measures has been a central concern of articles published in the Journal of Applied Psychology (JAP) since the inception of the journal. At the same time, it isn't clear that investigators and reviewers prioritize psychometric soundness to a degree that would allow one to have sufficient confidence in conclusions regarding constructs. The purposes of the present article are to (a) examine current scale development and evaluation practices in JAP; (b) compare these practices to recommended practices, previous practices, and practices in other journals; and (c) use these comparisons to make recommendations for reviewers, editors, and investigators regarding the creation and evaluation of measures including Excel-based calculators for various indices. Finally, given that model complexity appears to have increased the need for short scales, we offer a user-friendly R Shiny app (https://orgscience.uncc.edu/about-us/resources) that identifies the subset of items that maximize a variety of psychometric criteria rather than merely maximizing alpha. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

How can big data science transform the psychological sciences?

Big data and related technologies are radically altering our society. In a similar way, these approaches can transform the psychological sciences. The goal of this commentary is to motivate psychologists to embrace big data science for the betterment of the field. Big data sources, algorithmic methods, and a culture that embraces prediction has the potential to advance our science, improve the robustness and replicability of our research, and allow us to focus more centrally on actual behaviors. We highlight these key transformations, acknowledge criticisms of big data approaches, and emphasize specific ways psychologists can contribute to the big data science revolution

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From Outcome to Process Focus: Fostering a More Robust Psychological Science Through Registered Reports and Results-Blind Reviewing.

A variety of alternative mechanisms, strategies, and "ways of doing" have been proposed for improving the rigor and robustness of published research in the psychological sciences in recent years. In this article, we describe two existing but underused publication models-registered reporting (RR) and results-blind reviewing (RBR)-that we believe would contribute in important ways to improving both the conduct and evaluation of psychological research. We first outline the procedures and distinguishing features of both publication pathways and note their value for promoting positive changes to current scientific practices. We posit that a significant value of RR and RBR is their potential to promote a greater focus on the research process (i.e., how and why research is conducted) relative to research outcomes (i.e., what was observed or concluded from research). We conclude by discussing what we perceive to be five common beliefs about RR and RBR practices and attempt to provide a balanced perspective of the realities likely to be experienced with these systems.

The effects of social contact on cocaine intake in female rats.

BACKGROUND: Studies conducted in male rats report that social contact can either facilitate or inhibit drug intake depending on the behavior of social partners. The purpose of the present study was to: (1) examine the effects of social contact on cocaine intake in female rats, (2) examine the behavioral mechanisms by which social contact influences cocaine intake, and (3) examine whether the estrous cycle moderates the effects of social contact on cocaine intake. METHODS: Female rats were assigned to either isolated or pair-housed conditions in which a social partner either had access to cocaine (cocaine partner) or did not have access to cocaine (abstinent partner). Pair-housed rats were tested in custom-built operant conditioning chambers that allowed both rats to be tested simultaneously in the same chamber. RESULTS: Rats housed with a cocaine partner self-administered more cocaine than isolated rats and rats housed with an abstinent partner. A behavioral economic analysis indicated that these differences were driven by a greater intensity of cocaine demand (i.e., greater intake at lower unit prices) in rats housed with a cocaine partner. Multivariate modeling revealed that the estrous cycle did not moderate the effects of social contact on cocaine intake. CONCLUSIONS: These findings indicate that: (1) social contact influences cocaine self-administration in females in a manner similar to that reported in males, (2) these effects are due to differences in the effects of social contact on the intensity of cocaine demand, and (3) these effects are consistent across all phases of the estrous cycle.

The role of proximal social contexts: Assessing stigma-by-association effects on leader appraisals.

Prior research suggests that segregation in the U.S. workplace is on the rise (Hellerstein, Neumark, & McInerney, 2008); as such, leaders are more likely to lead groups of followers composed primarily of their own race (Elliot & Smith, 2001; Smith & Elliott, 2002). Drawing from theory on stigma-by-association, the authors posit that such segregated proximal social contexts (i.e., the leader's group of followers) can have detrimental effects on leader appraisals. Specifically, they argue that leaders of mostly Black follower groups experience stigmatization based on race stereotypic beliefs, which affects how they are viewed in the eyes of observers. The results of a large field study show performance evaluations generally tend to be lower when the proportion of Black followers is higher. Moreover, 3 experiments demonstrate that the impact of proximal social contexts extends to other outcomes (i.e., perceptions of market value and competency) but appears limited to those who are less internally and externally motivated to control their prejudice. Taken together, these findings explain how workplace segregation systematically can create a particular disadvantage for Black leaders.

Beyond step-down analysis: a new test for decomposing the importance of dependent variables in MANOVA.

Multivariate analysis of variance (MANOVA) is often categorized as a tool for experimental psychologists. However, it also continues to be a popular statistical procedure used by organizational scientists. Unfortunately, when the dependent variables (DV) are correlated with one another, interpreting the significant omnibus test in MANOVA becomes difficult. The present article proposes a novel way of interpreting a significant MANOVA that draws from work dedicated to understanding the relative importance of correlated predictors in multiple regression. Relative importance analyses are specifically designed to overcome the limitations caused by correlated variables and permit researchers to appropriately partition shared variance. We derive and extend relative weight analysis to MANOVA designs and demonstrate how these weights may be used to draw inferences concerning the relative contribution of each DV to the overall multivariate effect. Through our example, we illustrate how researchers must consider the correlations among the DVs when interpreting a significant multivariate effect, and our procedure provides an effective mechanism for doing just that.

Peer influences on drug self-administration: an econometric analysis in socially housed rats.

Social-learning theories of substance use propose that members of peer groups influence the drug use of other members by selectively modeling, reinforcing, and punishing either abstinence-related or drug-related behaviors. The objective of the present study was to examine the social influences on cocaine self-administration in isolated and socially housed rats, under conditions where the socially housed rats were tested simultaneously with their partner in the same chamber. To this end, male rats were obtained at weaning and housed in isolated or pair-housed conditions for 6 weeks. Rats were then implanted with intravenous catheters and cocaine self-administration was examined in custom-built operant conditioning chambers that allowed two rats to be tested simultaneously. For some socially housed subjects, both rats had simultaneous access to cocaine; for others, only one rat of the pair had access to cocaine. An econometric analysis was applied to the data, and the reinforcing strength of cocaine was measured by examining consumption (i.e. quantity demanded) and elasticity of demand as a function of price, which was manipulated by varying the dose and ratio requirements on a fixed ratio schedule of reinforcement. Cocaine consumption decreased as a function of price in all groups. Elasticity of demand did not vary across groups, but consumption was significantly lower in socially housed rats paired with a rat without access to cocaine. These data suggest that the presence of an abstaining peer decreases the reinforcing strength of cocaine, thus supporting the development of social interventions in drug abuse prevention and treatment programs.

Word selection affects perceptions of synthetic biology.

Members of the synthetic biology community have discussed the significance of word selection when describing synthetic biology to the general public. In particular, many leaders proposed the word "create" was laden with negative connotations. We found that word choice and framing does affect public perception of synthetic biology. In a controlled experiment, participants perceived synthetic biology more negatively when "create" was used to describe the field compared to "construct" (p = 0.008). Contrary to popular opinion among synthetic biologists, however, low religiosity individuals were more influenced negatively by the framing manipulation than high religiosity people. Our results suggest that synthetic biologists directly influence public perception of their field through avoidance of the word "create".

The Case for Use of Simple Difference Scores to Test the Significance of Differences in Mean Rates of Change in Controlled Repeated Measurements Designs.

A previous Monte Carlo study examined the relative powers of several simple and more complex procedures for testing the significance of difference in mean rates of change in a controlled, longitudinal, treatment evaluation study. Results revealed that the relative powers depended on the correlation structure of the simulated repeated measurements. Tests on dropout-weighted linear slope coefficients fitted to all of the available measurements for each participant were found to provide superior power in the presence of compound symmetry (CS), but tests of significance applied to simple baseline-to-endpoint difference scores provided superior power in the presence of a strongly autoregressive (AR) correlation structure. Type I error rates appeared in an acceptable range for both of those analyses. Insofar as the previous study considered only two widely disparate correlation structures, the present work was undertaken to examine where along a continuum of correlation structures lying between strongly AR and CS the power balance shifts from favoring the simple endpoint difference-score analysis to favoring a regression analysis that utilizes all of the available repeated measurements for each participant. With power calculated from the relative frequencies of rejecting Ho at different levels of autoregression, the results indicate superior power for the simple endpoint analysis across more than half the distance from strongly AR to CS. To examine replicability of the simulation results using real data from a previously published study, sampling with replacement from a double-blind controlled study examining the treatment of depression was used to create a Monte Carlo data set from which power could be calculated from relative frequencies of rejecting Ho.

Determining the statistical significance of relative weights.

Relative weight analysis is a procedure for estimating the relative importance of correlated predictors in a regression equation. Because the sampling distribution of relative weights is unknown, researchers using relative weight analysis are unable to make judgments regarding the statistical significance of the relative weights. J. W. Johnson (2004) presented a bootstrapping methodology to compute standard errors for relative weights, but this procedure cannot be used to determine whether a relative weight is significantly different from zero. This article presents a bootstrapping procedure that allows one to determine the statistical significance of a relative weight. The authors conducted a Monte Carlo study to explore the Type I error, power, and bias associated with their proposed technique. They illustrate this approach here by applying the procedure to published data.

SOMAS-URM: The Evolution of a Mentoring and Summer Research Program.

The need to enhance recruitment and retention of students in the sciences to strengthen the economic and scientific foundation of the United States was recently underscored by the National Science Board. The SOMAS Program (Support Of Mentors And their Students) addresses this need using a two-pronged strategy: 1) Junior faculty receive mentoring and instruction in launching research programs that engage student collaborators; and 2) College students are introduced to discovery in the neurosciences by conducting original research with their professors. Junior faculty from predominantly undergraduate institutions are invited to submit applications to obtain summer research support for undergraduate students who will spend 10 weeks collaborating with the faculty member on projects of common interest. Awards cover a travel and a supply budget, summer student housing, as well as faculty and student stipends. The faculty mentors and their students are to use the travel support to attend the joint Annual Meetings of the Society for Neuroscience (SfN) and the Faculty for Undergraduate Neuroscience (FUN). Faculty Awardees are required to participate in the Survival Skills and Ethics Workshop held at the SfN Meeting to prepare them to write grants aimed at supporting their research programs. Students are to present their summer research findings at the FUN Poster Session held jointly with the SfN Meeting. Students are also required to attend Survival Skills Workshop sessions that focus on ethics in research and that provide tips on applying to graduate school. The SOMAS-URM Program presently emphasizes recruitment and retention of underrepresented groups to enhance participation in scientific discovery by the full range of the American population.

Multivariate relative importance: extending relative weight analysis to multivariate criterion spaces.

For years, organizational scholars have sought effective ways to evaluate the importance of predictors included in a regression analysis. Recent techniques, such as general dominance weights and relative weights, have shown great promise for guiding evaluations of predictor importance. Nevertheless, questions remain regarding how one should investigate relative importance in the presence of a multidimensional criterion variable. The purpose of this article is to extend understanding of relative importance statistics to multivariate designs. The authors review the concept of relative importance and discuss a new procedure for calculating estimates of importance in the presence of multiple correlated criteria. Finally, a published correlation matrix is reanalyzed and a Monte Carlo simulation conducted to compare the new procedure with another technique for estimating importance. Unlike canonical solutions, which are often uninterpretable, the proposed multivariate relative weights provide an intuitive index regarding the relationship between predictors and criteria. Implications for organizational researchers are discussed.

Genome Consortium for Active Teaching: meeting the goals of BIO2010.

The Genome Consortium for Active Teaching (GCAT) facilitates the use of modern genomics methods in undergraduate education. Initially focused on microarray technology, but with an eye toward diversification, GCAT is a community working to improve the education of tomorrow's life science professionals. GCAT participants have access to affordable microarrays, microarray scanners, free software for data analysis, and faculty workshops. Microarrays provided by GCAT have been used by 141 faculty on 134 campuses, including 21 faculty that serve large numbers of underrepresented minority students. An estimated 9480 undergraduates a year will have access to microarrays by 2009 as a direct result of GCAT faculty workshops. Gains for students include significantly improved comprehension of topics in functional genomics and increased interest in research. Faculty reported improved access to new technology and gains in understanding thanks to their involvement with GCAT. GCAT's network of supportive colleagues encourages faculty to explore genomics through student research and to learn a new and complex method with their undergraduates. GCAT is meeting important goals of BIO2010 by making research methods accessible to undergraduates, training faculty in genomics and bioinformatics, integrating mathematics into the biology curriculum, and increasing participation by underrepresented minority students.

Rule-of-thumb adjustment of sample sizes to accommodate dropouts in a two-stage analysis of repeated measurements.

Recent contributions to the statistical literature have provided elegant model-based solutions to the problem of estimating sample sizes for testing the significance of differences in mean rates of change across repeated measures in controlled longitudinal studies with differentially correlated error and missing data due to dropouts. However, the mathematical complexity and model specificity of these solutions make them generally inaccessible to most applied researchers who actually design and undertake treatment evaluation research in psychiatry. In contrast, this article relies on a simple two-stage analysis in which dropout-weighted slope coefficients fitted to the available repeated measurements for each subject separately serve as the dependent variable for a familiar ANCOVA test of significance for differences in mean rates of change. This article is about how a sample of size that is estimated or calculated to provide desired power for testing that hypothesis without considering dropouts can be adjusted appropriately to take dropouts into account. Empirical results support the conclusion that, whatever reasonable level of power would be provided by a given sample size in the absence of dropouts, essentially the same power can be realized in the presence of dropouts simply by adding to the original dropout-free sample size the number of subjects who would be expected to drop from a sample of that original size under conditions of the proposed study.

A two-stage analysis of repeated measurements with dropouts and/or intermittent missing data.

This article is about a simple two-stage analysis that utilizes slope coefficients as the dependent variable for testing the significance of difference in mean rates of change in repeated measurement designs with missing data. The ANCOVA test on the doubly weighted slope coefficients provides power comparable to that of more complex maximum likelihood procedures when data are missing completely at random, requires fewer assumptions and is more generally applicable under realistic nonrandom dropout conditions, and most importantly can be readily understood and explained by those who actually do most controlled clinical research.

Use of resampling to select among alternative error structure specifications for GLMM analyses of repeated measurements.

Autocorrelated error and missing data due to dropouts have fostered interest in the flexible general linear mixed model (GLMM) procedures for analysis of data from controlled clinical trials. The user of these adaptable statistical tools must, however, choose among alternative structural models to represent the correlated repeated measurements. The fit of the error structure model specification is important for validity of tests for differences in patterns of treatment effects across time, particularly when maximum likelihood procedures are relied upon. Results can be affected significantly by the error specification that is selected, so a principled basis for selecting the specification is important. As no theoretical grounds are usually available to guide this decision, empirical criteria have been developed that focus on mode fit. The current report proposes alternative empirical criteria that focus on bootstrap estimates of actual type I error an power of tests for treatment effects. Results for model selection before and after the blind is broken are compared. Goodness-of-fit statistics also compare favourably for models fitted to the blinded or unblinded data, although the correspondence to actual type I error and power depends on the particular fit statistic that is considered.

Determining the number of clusters by sampling with replacement.

A split-sample replication criterion originally proposed by J. E. Overall and K. N. Magee (1992) as a stopping rule for hierarchical cluster analysis is applied to multiple data sets generated by sampling with replacement from an original simulated primary data set. An investigation of the validity of this bootstrap procedure was undertaken using different combinations of the true number of latent populations, degrees of overlap, and sample sizes. The bootstrap procedure enhanced the accuracy of identifying the true number of latent populations under virtually all conditions. Increasing the size of the resampled data sets relative to the size of the primary data set further increased accuracy. A computer program to implement the bootstrap stopping rule is made available via a referenced Web site.