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In recent years, there has been an alarming increase in both proposed and enacted legislation that targets diversity, equity, and inclusion (DEI) initiatives within higher education and organizations. These laws aim to dismantle protections and support for vulnerable individuals and groups. In this focal article, we provide an overview of the motives driving these anti-DEI legislative initiatives and categorize the laws based on the type of restrictions they impose: (a) reduction in knowledge, (b) reduction in access, and (c) reduction in support. Next, we discuss the consequences these anti-DEI laws yield for individuals, organizations, and society at large and provide an overview of how individuals and organizations may counteract these regressive policies. Last, we conclude with a call to action for I-O psychologists to investigate and call attention to the consequences of anti-DEI laws for recruitment and selection, well-being and safety of minoritized individuals, organizational reputation, and organizational performance and profits.

Racism, sexism, and ageism persist in modern day organizations and may translate into workplace discrimination, which can undermine organizational effectiveness. We provide the first meta-analysis comparing the relationships between these three types of prejudice (racism, sexism, and ageism) and three types of workplace discrimination (selection, performance evaluation, and opposition to diversity-supportive policies). Across outcomes, racism was associated with workplace discrimination, whereas sexism was not. Ageism was associated with discriminatory selection and opposition to organizational policies supporting older workers; however, ageism was not related to discriminatory performance evaluation. Consistent with prior research and theory, Implicit Association Test measures were related to subtle discrimination (opposition to diversity-supportive policies) but not deliberate discrimination (selection and performance evaluation). Finally, prejudice was more strongly associated with discrimination against real as compared with hypothetical targets. Implications for organizational researchers and practitioners are discussed.

Despite a large proportion of working mothers in the American workforce, research suggests that negative stereotypes and discrimination against working mothers continue to exist. In a set of two experimental studies, the current paper examined subtle discrimination against non-pregnant, working mothers in different hiring settings. In Study 1, using a between-subject field experiment and applying for geographically dispersed jobs with manipulated resumes, we found evidence for subtle discrimination, such that mothers received more negativity in callback messages than women without children, men without children, and fathers. They were also rejected more quickly than women without children and fathers. In Study 2, using a more controlled experimental paradigm, we tested our hypothesis in a hypothetical interview evaluation setting. We found that mothers faced more interpersonal hostility across different job types as compared to women without children. Together, these studies highlight the presence of subtle discrimination against working mothers at different stages of the hiring process.

Pregnancy represents a critical time during which women are increasingly susceptible to challenges that can shape maternal health postpartum. Given the increasing number of women who are working through the duration of their pregnancies, in this study, we examine the extent to which both maternal psychological and physical health are influenced by social support received at work during pregnancy. Specifically, we examine 118 pregnant employees’ perceptions of coworker support, supervisor support, and stress over the course of 15 working days. We then link prenatal stress levels with postpartum maternal health outcomes following women’s return to work. At the within-person level, coworker support predicted next-day decreases in stress during pregnancy; however, stress did not predict next-day change in coworker support. There was no relationship between supervisor support and next-day change in stress during pregnancy or vice versa. At the between-person level, an interactive effect between coworker support and supervisor support emerged in predicting prenatal stress, such that women who benefitted from supportive coworkers and supportive supervisors during pregnancy reported the lowest levels of prenatal stress which were, in turn, associated with lower incidence of postpartum depression and quicker recovery times from birth-related injuries. Significant indirect effects suggested that when perceptions of supervisor support were higher (but not lower), coworker support during pregnancy predicted lower incidence of postpartum depression and quicker recovery times through reduced prenatal stress. Taken together, our findings provide novel insight into how specific aspects of the workplace environment may interact to shape maternal psychological and physical health during pregnancy and postpartum.

Due to rising pressure to appear egalitarian, subtle discrimination pervades today's workplace. Although its ambiguous nature may make it seem innocuous on the surface, an abundance of empirical evidence suggests subtle discrimination undermines employee and organizational functioning, perhaps even more so than its overt counterpart. In the following article, we argue for a multidimensional and continuous, rather than categorical, framework for discrimination. In doing so, we propose that there exist several related but distinct continuums on which instances of discrimination vary, including subtlety, formality, and intentionality. Next, we argue for organizational scholarship to migrate toward a more developmental, dynamic perspective of subtle discrimination in order to build a more comprehensive understanding of its antecedents, underlying mechanisms, and outcomes. We further contend that everyone plays a part in the process of subtle discrimination at work and, as a result, bears some responsibility in addressing and remediating it. We conclude with a brief overview of research on subtle discrimination in the workplace from each of four stakeholder perspectives—targets, perpetrators, bystanders, and allies—and review promising strategies that can be implemented by each of these stakeholders to remediate subtle discrimination in the workplace.

Purpose The purpose of this paper is to understand the implications of anticipated discrimination for women and racial minorities when they lose out on an opportunity for a promotion to a similarly qualified non-minority colleague. Design/methodology/approach A sample of 248 participants who were full-time working adults residing in the USA were randomly assigned to one of four versions of the scenario in which a coworker was either a White male, a White female, a Black male or a Black female coworker is offered a desired promotion. Participants reported on the extent to which they anticipated discrimination (i.e. expect discriminatory behaviors enacted toward them in the future) in the hypothetical workplace. Findings Women and racial minorities reported anticipated discrimination at greater levels than non-minorities when passed over for a promotion. The authors also found that intersectionally stigmatized, racial minority women reported the highest levels of anticipated discrimination. Practical implications The authors recommend transparent and honest communication about organizations’ decision-making processes that have career-related implications for underrepresented populations. Doing so may help alleviate concerns or perceptions that employees may have in regard to organizational practices being (intentionally or unintentionally) discriminatory. Originality/value While research has examined the psychological implications of receiving a promotion, substantially less work has focused on the characteristics of the promoted coworker or considered how those characteristics shape perceptions of anticipating discrimination.

The decision to express a stigmatized identity inside and outside of the workplace is highly complex, with the potential for both negative and positive outcomes. This meta-analysis examines the intrapersonal and interpersonal workplace and non-workplace outcomes of engaging in this identity management strategy. Synthesizing stigma and relationship formation theories, we hypothesize and test boundary conditions for these relationships including the visibility and controllability of the stigma, the study setting, and the gender of the interaction partner. Through our analysis of 65 unique samples (k = 108), we find that expression is more likely to lead to beneficial outcomes in interpersonal, workplace, and non-workplace domains, but only for less-visible stigmas and for studies conducted within a field vs. lab setting. Finally, we explore stigma expression across specific stigmatized identities and determine that there are consistently positive outcomes of expression for individuals with stigmatized religious and sexual orientation identities.

The experiences of pregnant employees in the American workforce is a central topic of conversation in current sociopolitical discourse. While previous research focuses more on how pregnant employees can shape others’ impressions of themselves, we shift the lens to explore the intrapersonal experiences of pregnant employees. Using a weekly survey methodology, we examined the extent to which utilizing different coping strategies (active coping and denial) shapes perceptions of both time-based pregnancy-to-work (PWC) and work-to-pregnancy conflict (WPC) over a period of four weeks. The results suggest that using denial as a coping strategy led to increased PWC the following week. Additionally, we found evidence that conflict felt during pregnancy spilled over to predict lower positive affect six months postpartum. We provide some of the first data on the influence of the intrapersonal experiences of pregnant employees not only during their time at work, but also when returning to work postpartum.

PurposeThe authors aim to broaden the understanding of incivility through the lens of bystanders who witness incivility toward women. Integrating attributional ambiguity and emotional contagion theories with the literature on workplace mistreatment, the authors propose that witnessing incivility toward women may negatively impact bystanders.Design/methodology/approachThe authors collected multi-wave data from 324 employees to assess the consequences of witnessing incivility toward women at work for bystanders.FindingsUtilizing a serial mediation model, the authors found evidence that witnessing incivility toward women indirectly increased turnover intentions six weeks later, first through elevated negative affect and then through increased cognitive burnout.Originality/valueTaken together, this study's findings suggest that the negative effects of incivility toward women can spread to bystanders and highlight the importance of considering individuals who are not directly involved, but simply bear witness to incivility at work.

Magnetic resonance imaging (MRI) of the knee is the preferred method for diagnosing knee injuries. However, interpretation of knee MRI is time-intensive and subject to diagnostic error and variability. An automated system for interpreting knee MRI could prioritize high-risk patients and assist clinicians in making diagnoses. Deep learning methods, in being able to automatically learn layers of features, are well suited for modeling the complex relationships between medical images and their interpretations. In this study we developed a deep learning model for detecting general abnormalities and specific diagnoses (anterior cruciate ligament [ACL] tears and meniscal tears) on knee MRI exams. We then measured the effect of providing the model's predictions to clinical experts during interpretation. Our dataset consisted of 1,370 knee MRI exams performed at Stanford University Medical Center between January 1, 2001, and December 31, 2012 (mean age 38.0 years; 569 [41.5%] female patients). The majority vote of 3 musculoskeletal radiologists established reference standard labels on an internal validation set of 120 exams. We developed MRNet, a convolutional neural network for classifying MRI series and combined predictions from 3 series per exam using logistic regression. In detecting abnormalities, ACL tears, and meniscal tears, this model achieved area under the receiver operating characteristic curve (AUC) values of 0.937 (95% CI 0.895, 0.980), 0.965 (95% CI 0.938, 0.993), and 0.847 (95% CI 0.780, 0.914), respectively, on the internal validation set. We also obtained a public dataset of 917 exams with sagittal T1-weighted series and labels for ACL injury from Clinical Hospital Centre Rijeka, Croatia. On the external validation set of 183 exams, the MRNet trained on Stanford sagittal T2-weighted series achieved an AUC of 0.824 (95% CI 0.757, 0.892) in the detection of ACL injuries with no additional training, while an MRNet trained on the rest of the external data achieved an AUC of 0.911 (95% CI 0.864, 0.958). We additionally measured the specificity, sensitivity, and accuracy of 9 clinical experts (7 board-certified general radiologists and 2 orthopedic surgeons) on the internal validation set both with and without model assistance. Using a 2-sided Pearson's chi-squared test with adjustment for multiple comparisons, we found no significant differences between the performance of the model and that of unassisted general radiologists in detecting abnormalities. General radiologists achieved significantly higher sensitivity in detecting ACL tears (p-value = 0.002; q-value = 0.019) and significantly higher specificity in detecting meniscal tears (p-value = 0.003; q-value = 0.019). Using a 1-tailed t test on the change in performance metrics, we found that providing model predictions significantly increased clinical experts' specificity in identifying ACL tears (p-value < 0.001; q-value = 0.006). The primary limitations of our study include lack of surgical ground truth and the small size of the panel of clinical experts. Our deep learning model can rapidly generate accurate clinical pathology classifications of knee MRI exams from both internal and external datasets. Moreover, our results support the assertion that deep learning models can improve the performance of clinical experts during medical imaging interpretation. Further research is needed to validate the model prospectively and to determine its utility in the clinical setting.