Appendix B

Understanding the Causes of Burnout and Gender and Race Disparities in STEMM: A Multilevel Approach

Youngjoo Cha, Cassie Mead

This paper was commissioned by National Academies of Sciences, Engineering, and Medicine’s Committee on Women in Science, Engineering, and Medicine. Opinions and statements included in the paper are solely those of the individual authors, and are not necessarily adopted, endorsed, or verified as accurate by the committee or the National Academies of Sciences, Engineering, and Medicine.

INTRODUCTION

Many workers experience work-related stress, with 57 percent of U.S. workers experiencing emotional exhaustion and mental disengagement from work, which are both prominent symptoms of burnout (American Psychological Association, 2023). Given that burnout is strongly linked to negative physical and mental health outcomes, reduced work productivity, lower job satisfaction, and higher job turnover rates, the prevalence of burnout among the majority of the U.S. workforce is alarming (Ahola et al., 2005; Borritz et al., 2010; Ducharme et al., 2007; Melamed et al., 2006; Moen et al., 2016; Salvagioni et al., 2017; von Känel et al., 2020). Why do people experience burnout at such high rates in today’s workplaces? What causes burnout, and who is most affected? How might burnout risk factors explain gender and race disparities in science, technology, engineering, mathematics, and medicine (STEMM) fields? In this paper, we provide

comprehensive reviews of research that address these questions. We argue that burnout is a multilevel phenomenon that operates at the macro, meso, and micro levels and organize our reviews around this framework. While most empirical studies in the literature on burnout focus on individual-level (micro) and job and workplace (meso) factors, we also consider structural (macro) factors, which help to understand the underlying causes of burnout widespread in contemporary workplaces.

A multilevel framework is frequently employed to theorize key concepts in the social sciences, such as gender (Ridgeway and Correll, 2004) employment discrimination (Hirsh and Cha, 2008), and imprinting (Marquis and Tilcsik, 2013). In the context of burnout, a similar approach is outlined in the National Academy of Medicine’s 2019 Consensus Study Report on clinician burnout, where burnout factors are categorized into: “frontline care delivery” (local contexts where clinicians- patients interactions occur), “health care organizations,” or “external environment” (economic and regulatory environments). However, this framework differentiates the first two levels based on the relevance to the clinician-patient interactions, rather than the specific units in which the factors reside, as we do.

We distinguish macro-, meso-, and micro-level factors based on the contexts in which risk factors arise. Macro-level factors include (1) macroeconomic changes that have increased job demands and reduced job security; (2) cultural shifts in the valuation of work, which have raised the expectations for constant availability for work; and (3) demographic changes that have intensified work-family conflicts. These structural changes in the U.S. labor market have created a foundation for burnout risk factors operating at the meso and micro levels.

Meso-level factors operate at job and workplace levels. They include work conditions linked to a higher risk of burnout, such as increased job demands, a lack of control over work activities and schedules, and the use of communication technologies. Much of the recent literature concentrates on these meso-level factors.

Micro-level factors operate at individual and interactional levels, including workers’ identification with their jobs, perceived cultural fit, interactional-level inequalities, and emotional labor. Research indicates that strong identification with work can help mitigate the effects of factors at the meso-level that make STEMM workers more susceptible to burnout than other workers (e.g., higher job demands). However, these shielding effects tend to diminish among women and minoritized workers, whose cultural fit

is often questioned due to pervasive stereotypes and gendered and racialized cultural ideals in STEMM fields.

These factors interact with one another, often across different levels, moderating their effects on burnout outcomes. For example, the negative impact of increased workloads can be mitigated through workplace practices and policy redesign. The impact of workloads can also be moderated by how individuals identify with their work. In each section, we will discuss these cross-level moderating relationships.

Finally, we offer a conceptual tool to understand the effects of these factors on women and minoritized workers, categorizing burnout factors identified in the literature into two categories. First, some factors appear to be gender- and race-neutral but produce gender- and race-specific outcomes. For example, poor working conditions that contribute to burnout may seem gender- and race-neutral, since anyone who is exposed to them, regardless of their gender and race, can experience burnout. However, due to persistent gender and racial job segregation, where White men tend to occupy positions with more desirable working conditions, these factors affect disproportionately women and minoritized workers compared with their White male counterparts (Glass, 2004; Stainback and Tomaskovic-Devey, 2012; Stier and Yaish, 2014).

The second group of factors directly relates to gendered and racialized processes. These include racism and sexism that operate at the interactional level as well as workplace norms and practices grounded upon highly gendered and racialized assumptions—often implicitly favoring White men’s bodies, lifestyles, and cultural images. Such dynamics produce structural disadvantages for women and minoritized workers. Experiences of discrimination and mistreatment, along with a lack of belonging, can expose these workers to a higher risk of burnout.

Many empirical studies that we discuss focus specifically on STEMM fields, while others, although not directly based on STEMM data, provide insights into general processes associated with burnout that are highly relevant to STEMM.¹ At the end of the paper, we summarize the key points and suggest directions for future research.

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¹ Our literature review is based on three key sources: (1) influential studies in the relevant fields, (2) a literature search from select high-impact journals in sociology, human resources/management, psychology, and health, using keywords such as “burnout,” “STEM,” and “overwork,” and (3) a snowball search of studies cited in the articles identified through (1) and (2).

WHAT CAUSES BURNOUT? MULTILEVEL APPROACH

Macro-Level Factors: Economic, Cultural and Demographic Shifts

Much of the literature identifying the causes of burnout focuses on meso- and micro-level factors. However, these factors have emerged as prominent risk factors against the backdrop of broader macrostructural shifts in the U.S. economy over the past half-century. These macro-level factors have set the stages for meso- and micro-level factors that dominate burnout research. In our view, understanding these macro-level factors is essential to grasp why burnout is so widespread in contemporary workplaces, affecting not just specific groups of workers or types of workplaces.

Macroeconomic Conditions

Research has long shown that today’s workers at the upper rung of the occupational hierarchy are working longer hours than workers from a half-century ago (Cha and Weeden, 2014; Jacobs and Gerson, 2004; Kalleberg, 2011). To understand the underlying causes of rising work hours in high-skilled high-paying jobs, scholars highlight structural shifts in macroeconomic conditions in the labor market. These include globalization and downsizing, which have made jobs less secure, the compensation system that disproportionately rewards those who work long hours (e.g., tournament promotion system; Epstein et al., 1999; Landers et al., 1996), and the adoption of productivity-enhancing technologies (Beckman and Mazmanian, 2020; Perlow, 2012).

Growing global competition in product markets has led to widespread corporate restructuring in the U.S. economy. Along with the decline of unions and diffusion of shareholder value systems, these changes have fundamentally reshaped employment relations, making them less secure and more fluid (Fligstein and Shin, 2004; Kalleberg, 2011). These changes have stratified the workforce into core employees, whose job demands increased, and contingent workers, whose positions are temporary, and contract based (Kalleberg, 2001, 2011; Tomaskovic-Devey and Avent-Holt, 2019). As a result, job quality in the U.S. economy has diverged (Jacobs and Gerson, 2004; Kalleberg, 2011). Those workers who “survived” downsizing and remain as core employees enjoy relatively desirable working conditions, with higher pay, greater job autonomy, and schedule flexibility, but they often face long and intense work hours. By contrast, those who were laid

off or displaced from jobs are now in more precarious employment, with lower pay, fewer job benefits and legal protections, and reduced job autonomy. The employment and financial instability experienced by contingent workers is strongly linked to poorer mental health outcomes (Glavin and Schieman, 2022).

Given the high demand for STEMM skills, a relatively high proportion of STEMM workers remain in the core sector. However, widespread downsizing has increased their workloads. Staff shortage is a widespread issue in the current economy, with even workers in noncompetitive public-sector jobs reporting frequently working beyond contracted 40-hour workweeks due to chronic staffing shortages (Lee et al., 2024). STEMM jobs are no exception. In a study of 10,184 nurses and 232,342 patients across 168 hospitals, Aiken et al. (2002) found that the major factor behind nurses’ increased work hours was staff shortages. The increased workload for nurses also contributes to burnout, with each additional patient per nurse associated with a 23 percent increase in the odds of burnout. Furthermore, workplace downsizing often requires workers to learn additional skills to meet a wider range of job demands, further elevating the risk of burnout (Lebel et al., 2023).

The number of contract workers within the STEMM workforce has also been steadily rising as the economy shifts toward more flexible employment relationships (Kunda et al., 2002; Wingfield, 2019). This trend means that today’s STEMM workers are less shielded from employment and financial instability than their counterparts were several decades ago. In a study of high-skilled technical contractors, Kunda et al. (2002) found that despite lucrative pay, contract workers frequently reported feeling anxious about uncertainties of their next contracts and a sense of being an “outsider,” which negatively affected their psychological well-being.

These structural changes have not only increased the number of work hours but also the intensity of these hours. Using data from the 2002 National Study of Changing Workforce, Kalleberg (2011) shows that between 1977 and 2002, the proportion of workers reporting that they had too much work to do and that their jobs required that they work very fast and very hard increased significantly. Specifically, these metrics rose by 0.07 to 0.5 points on a 4-point scale.

In addition to these macrostructural changes, some scholars also highlight the diffusion of compensation systems in which workers’ relative standing, rather than their absolute performance, determines their promotion chances and pay raise, for example, “up or out” systems in laws,

academia, accounting, and executive pay (Biggart and O’Brien, 2010; Blair-Loy, 2003; Epstein et al., 1999; Kuhn and Lozano, 2008; Landers et al., 1996; Sharone, 2004). Under these systems, workers are pressured to provide extra evidence for their work commitment to differentiate themselves from their similarly performing colleagues, often resorting to long hours and providing around-the-clock availability for job demands.

Work hours and on-call expectations have further increased with the adoption of remote work and mobile communication technologies at work. These technologies have enabled economic activities to continue 24/7 around the globe, blurred the boundaries between work and home life, and exposed workers to a higher risk of burnout (Beckman and Mazmanian, 2020; Perlow, 2012).

Cultural Shift Toward the Ideal Worker Norm

Not only have job demands increased over the past half-century, but meanings attached to work hours have also become more normative. Across American workplaces, the prevailing view is that “ideal workers” are those who put in long hours, prioritize work above all else, and make themselves available at all times (Williams, 2001). The emergence of this ideal worker norm has pressured workers to put in more hours at work than they desire, driven both by fear of losing jobs or missing career opportunities and by a desire to meet the internalized personal standards or career goals (Blair-Loy, 2003; Lee et al., 2024). However, a large body of research indicates that the ideal worker norm can be harmful, intensifying work-life conflicts and deteriorating the health and well-being of employees (Cech and Blair-Loy, 2014; C. Collins, 2019; Kelly et al., 2010; Kleiner et al., 2015; Schieman et al., 2009; Stone, 2007; Stone and Lovejoy, 2021).

Furthermore, the emphasis of prioritizing work under strong ideal worker norms fosters a culture that devalues self-care. Experimental evidence shows that employees who leave work early for nonwork matters (e.g., childcare, personal care) are viewed negatively— seen as less committed to work, less likable, and less deserving of organizational rewards than employees who prioritize work (Sanzari et al., 2021). The study also shows that employees who leave work early for personal care reasons were viewed more negatively than those who do so for childcare needs. These findings suggest that the ideal worker norm not only pressures workers to prioritize job demands over other responsibilities but also creates a culture that neglects self-care.

The ideal worker norms are particularly pronounced in STEMM professions (Cech and Blair-Loy, 2014). STEMM workplaces often offer resources to help workers cope with increased job demands, such as paid time off and flexible work schedules, but the ideal worker norms can diminish the effectiveness of such resources. In contexts with strong ideal worker norms, taking advantage of policies designed to promote “work-life balance” is often viewed as a sign of lacking commitment to work, leading to negative evaluations of employees who use these resources (Kelly et al., 2010; J. C. Williams et al., 2013). This phenomenon, known as “flexibility stigma” (J. C. Williams et al., 2013), has been well-documented. Experimental evidence indicates that workers who use paid time off, work from home, and work flexible hours are perceived as less committed, less respected, and less deserving of promotions (Judiesch and Lyness, 1999; Munsch, 2016; Rudman and Mescher, 2013). The fear of experiencing penalties for using these policies also suppresses employee uptake (Cha and Grady, 2024; Gerstel and Clawson, 2014; Wynn and Rao, 2020). In STEMM contexts, numerous studies of healthcare workers show that taking sick leave is often seen as neglecting patient care, which is internalized by many healthcare workers, leading them to be reluctant to take time off for self-care or personal matters (Gerstel and Clawson, 2014; Kellogg, 2009).

This heightened flexibility stigma is damaging to employees’ health and contributes to burnout. Cech and Blair-Loy (2014) show that among academic scientists and engineers at the top research universities, the belief that pursuing work-life balance is stigmatized within their department is associated with higher turnover intentions, lower job satisfaction, and increased experiences of work-family conflicts.

Cultural Shift Toward Intensive Parenting

Most literature focuses on increased job demands and changes in work conditions as the major contributor to workplace burnout (see the next section). However, workers’ work lives are closely intertwined with their nonwork lives, and it is essential to consider family factors to fully understand the underlying causes of burnout.

Scholars have long recognized that the family is “greedy” in the same way that work is, demanding complete and undivided devotion of time and energy to meet family members’ needs (Blair-Loy, 2003; Coser, 1967; Jacobs and Gerson, 2004). This normative conception is reflected in contemporary parenting practices and philosophies. Hays (1996, p. 8) argues

that contemporary parenting emphasizes “child-centered, expert-guided, emotionally absorbing, labor intensive, and financially expensive” practices. Lareau (2003) similarly conceptualizes White middle-class parenting as “concerted cultivation,” characterized by organized activities and interactive communication. Recent studies find that these parenting practices have become prevalent across all race and class backgrounds (Ishizuka, 2019). These contemporary parenting styles require more time and cognitive energy from parents. While the intensification of parenting is less explored in the literature on burnout, we believe that this cultural shift toward intensive parenting contributes to work-family conflicts and feelings of time deficit among working families, both of which are linked to burnout (see Kossek and Ozeki, 1999, for a review).

The intensification of parenting is likely to affect women more than men. Although “involved fatherhood” has emerged as a new cultural ideal, parenting and childrearing are still more strongly expected from and taken by women (Blair-Loy, 2003; Calarco, 2024; Daminger, 2019). Daminger (2019) shows that among middle-class couples, women handle the majority of the “cognitive labor” associated with intensive parenting, such as managing schedules, selecting childcare, and setting and monitoring children’s daily routines. This gendered effect of intensive parenting norms may increase women’s risk of experiencing family-to-work conflict and mental disengagement from work as a coping strategy (Aldossari and Chaudhry, 2021; Watts, 2009).

Demographic Shift in the Workforce

Another major structural source of workplace burnout is the demographic shift toward dual-earner households. Jacobs and Gerson (2004) show that the increase in work hours is more pronounced at the family level rather than at the individual level. Specifically, the joint weekly work hours among heterosexual married couples increased from 52.5 hours in 1970 to 63.1 hours by 2000, an increase of more than 10 hours. The increase in family-level work hours is largely driven by women’s rising labor force participation and full-time employment (Bianchi et al., 2012; Jacobs and Gerson, 2004).

Numerous studies turn to this striking increase in work hours at the family level as a major structural source of the rising number of Americans who feel overworked, burnt out, and time pressured (Bianchi et al., 2006; Matos and Galinsky, 2011). Scholars have long argued that workplace

norms and practices are built upon the implicit assumption that workers have “backstage support” (Hochschild and Machung, 1989)—a person, such as a stay-at-home spouse, who manages nonwork responsibilities, allowing workers to focus solely on their paid work. When the majority of the workforce does not fit the description of this prototype, imposing the ideals rooted in this assumption inevitably creates work-family conflicts (J. Williams, 2001).

This mismatch is more consequential for women, as most men still receive more spousal/partner support than women. Among full-time workers, many women have husbands working full-time, often with long hours, while a higher proportion of men have stay-at-home wives or wives working part-time. Among professional and managerial workers from dual-earner marriages, nearly 30 percent of women have husbands working 50 or more hours per week, compared with 13 percent of men (Cha, 2010). This disparity is especially pronounced among “super-rich couples”—those in top 1 percent in the income and wealth bracket—where the male breadwinner–female homemaker model is more common than among couples in the top 20 percent bracket (Yavorsky et al., 2023). Given that most STEMM workers are in higher income brackets, these findings suggest that STEMM women are more likely to have overworking spouses who contribute less to housework and childcare. This also means that STEMM women typically compete with STEMM men, who are generally more likely to have more spousal support.

Meso-Level Factors: Work Conditions and Practices

The impact of the structural factors discussed above manifests within specific organizational and occupational contexts, which a large body of literature examines. These meso-level factors—work conditions related to job demands and resources, along with workplace policies and practices that determine them—can moderate the effect of macro-level factors on an individual’s burnout outcomes.

Job Demands

Increased job demands have been at the center of the literature. Overall, research identifies three key drivers of burnout related to job demands in high-skilled jobs: long and intensified work hours, inflexible work schedules, and increased multifunctionality of job roles (Jacobs and Winslow, 2004; Kleiner and Pavalko, 2010).

Numerous studies link long hours to negative health outcomes (e.g., Jacobs and Winslow 2004; Kleiner and Pavalko, 2010). Kleiner and Pavalko (2010) show that individuals who work between 40 and 59 hours per week report significantly higher rates of depression and poorer mental health, compared with those who work standard 40-hour work weeks. Long hours have also been identified as a major source of job dissatisfaction among academic faculty. In a large nationally representative study of full-time postsecondary faculty members, Jacobs and Winslow (2004) find that about 50 percent of men and 60 percent of women who worked more than 60 hours a week reported being either “dissatisfied” or “very dissatisfied” with their jobs, compared with 29 percent of men and 38 percent of women who worked less than 50 hours a week.

Similarly, studies find that reducing job demands in “greedy” occupations can help prevent burnout. A quasi-experiment study of surgical residents shows that restructuring the surgical resident program designed to reduce workloads improved burnout outcomes (Hutter et al., 2006). Specifically, 1 year after the program changes—reducing on-call duties from every third night to every fourth night, allowing calls from home, and implementing cross-covering—the average burnout score among surgical residents, measured by the Maslach Burnout Inventory, decreased approximately 20 percent, moving from “high” (29.1) to “medium” (23.1).

Work-Work Conflict

An emerging body of literature identifies multiple roles and tasks required in a job as an important source of mental strain among high-skilled workers. For example, academic faculty often juggle between competing demands from multiple job functions—research, teaching, and administrative service. Wynn et al. (2018) introduce the concept of “work-work conflict,” to describe the strain that these inter-role conflicts create, which they find to be a major factor associated with burnout and lower job satisfaction among physicians and faculty in an academic medical center.

Another related source of inter-role conflict arises from being part of multiple teams in the workplace. A study of knowledge workers at a large research organization shows that multiple team memberships, especially switching between project teams, are associated with higher-level job stress and emotional exhaustion, resulting in a 34 percent increase in turnover rates (van de Brake et al., 2024).

Women and minoritized academic faculty are more prone to this inter-role conflict due to higher likelihood of holding joint affiliations and taking on cross-department assignments. In a study of an R1 university, Tian and Smith (2024) found that Black assistant professors were 2.08 times more likely to have dual appointments than their White counterparts. This disparity is also linked to lower retention rates among Black professors compared with their White peers.

Hidden Work

Some work activities of STEMM workers are often less visible and are seen merely “extra,” yet occupy much of their already tight schedules. Tasks categorized under “service” in academic sciences include a wide range of responsibilities, including administrative service for their department and university, additional leadership roles within universities or professional organizations, discipline-specific service, professional networking, and public outreach. One study finds that faculty spend on average about 9 hours per week in these service activities (Guarino and Borden, 2017).

Women and minoritized workers are more likely to take on this “unseen” work than their White men counterparts. Guarino and Borden (2017) find that women faculty spend on average 0.6 more hours per week than men. A study analyzing 33,456 resident physicians’ evaluation records across eight U.S. hospitals shows that women attending physicians spend more time on providing more detailed and constructive feedback to medical residents than men attending physicians (Nelson et al., 2023).

Women and minoritized workers also frequently take on multiple administrative roles or serve on taskforce committees related to efforts to expand STEMM access, in addition to their regular job duties. The increased service load is a risk factor for burnout among these workers. In the essay that appeared in Molecular Biology of the Cell, Trejo (2020) argues that the rise of the DEI initiatives in academic institutions increases the service burden of minority faculty, which she describes as a new form of the “minority tax.”

Technology

The adoption of remote work and communication technologies has enhanced worker productivity and provided flexibility in when and

where employees work (Kalleberg, 2016). At the same time, however, mobile technologies also increase the permeability of work and blurred the lines between work and home life. Work does not stop when workers leave their office, since employers, coworkers, and clients can reach them outside of business hours. Remote work technologies can also encourage employees who fully embrace a “workaholic” mentality to work even more. This boundary spanning has been shown to increase feelings of guilt and work-family conflicts, particularly among women (Glavin et al., 2011; Kossek et al., 2006).

Based on ethnographies of nine families with school-age children, Beckman and Mazmanian (2020) show that communication technologies exacerbate feelings of being perpetually behind and lacking control, while reinforcing myths of the ideal worker and ideal parents. Similarly, a case study analyzing detailed communication logs and surveys from 74 workers across three departments (engineering, marketing, and technical writers) finds that the time employees spend on emails is positively associated with perceptions of overload—emotional exhaustion, burnout, stress, and frustration from work (Barley et al., 2011). However, the author notes that time spent on emails reflects larger structural problems, such as workplace norms that create time pressure and an inefficient structure of work. That is, the volume of emails is a symptom of burnout rather than its cause.

Types of Tasks

Repetitive and routinized tasks are recognized as important stressors, so engaging in less routinized and high-creativity tasks in STEMM jobs can help alleviate some of the burnout risks (Schieman and Young, 2010). However, while those who engage with creative work are less likely to report work-related stress, the burnout-reducing effects of creative tasks can diminish in many professional occupations, because creative tasks often involve frequent boundary spanning and multitasking, which are risk factors of burnout (Schieman and Young, 2010).

Moreover, technology adoption and the rise of big data have increased the prevalence of repetitive and detail-oriented tasks in science and technical occupations. Bruns and Lingo (2024) show that this increase in “tedious” work results in time drain, disengagement, and information overload among academic scientists. These tasks are often assigned to younger, lower-ranked workers, whom other research has identified as being a high-risk group for burnout (e.g., Marchand et al., 2018).

Control Over Work Activities

Another important dimension that determines the quality of work is whether employees have control over work activities (Kalleberg, 2011). This is often defined as the extent to which employees can exercise autonomy or discretion in how to perform their jobs. The literature identifies inability to influence decision-making or plan one’s own work activities as important stressors that increase the risk of burnout (Karasek, 1979; Maslach and Leiter, 2008; Maslach et al., 2001). Conversely, providing workers with job control can help mitigate this risk. In a study of Italian healthcare workers, higher perceived job control was associated with lower levels of exhaustion among those reporting high workload (Portoghese et al., 2014). More broadly, however, high-skilled professional and managerial occupations tend to offer greater job autonomy compared with lower-skilled jobs. In this context, tasks commonly performed in most STEMM jobs are less associated with burnout risk.

Inflexible Schedules

The literature indicates that inflexible hours are major risk factors for burnout. Duffee and Willis (2023) show that the unpredictable nature of ambulance paramedics’ jobs limits their control over work schedules, which is a major predictor of high job stress.

However, organizational practices that give employees more control over work schedules, such as flextime, remote work, and paid time off, can help to prevent burnout. Studies of information technology (IT) employees at Fortune 500 companies show that workplace initiatives granting employees greater control over their work schedules reduce burnout and psychological distress (Kelly et al., 2011, 2014; Moen et al., 2016). Notably, these interventions did not reduce employees’ work hours, suggesting that it is not the number of hours worked but rather how those hours are structured that changed employees’ psychological health outcomes. Similarly, a study of healthcare workers found that adjusting work schedules in ways to support employees’ nonwork demands resulted in higher quality of patient care outcomes (Kossek et al., 2020).

Moderating Relationships: The Role of Job Demands and Resources in STEMM

Much of the literature investigating the role of working conditions emphasizes the moderating relationships among job conditions that often

offset each other’s effects. The upshot of these ideas is that while higher job demands can negatively impact workers’ well-being, job resources can buffer some of these adverse effects (“job demands-resources model”). More specifically, research identifies managerial support, job authority, job autonomy, creative tasks, higher earnings, and schedule control as important resources that help mitigate burnout (Bakker and Demerouti, 2007; Crawford et al., 2010; Karasek, 1979; Schieman et al., 2009).

Workplace support is consistently shown to be highly influential for workers’ engagement, psychological well-being, and outcomes in the work-family interface (Ducharme and Martin, 2000; Moen et al., 2016; O’Connor and Cech, 2018; Walsh, 2013). A study based on a national survey of hospital employees in England shows that while burnout rates are high among doctors, perceptions of burnout are contingent upon organizational support: specifically, coworkers’ support for women and family-friendly work culture and management support for men (Walsh, 2013). Similarly, O’Connor and Cech (2018) show that coworkers’ and supervisors’ support moderate the negative relationship between perceived flexibility stigma and job satisfaction and work-life conflicts. Ducharme and Martin (2000) find that both affective (emotional) support and instrumental (tangible aid) support were positively associated with job satisfaction.

Another strand of research turns to the role of job autonomy and schedule flexibility as a key resource that can mitigate the “harms” of long hours (De Moortel et al., 2017; Kaduk et al., 2019; Kelly et al., 2011, 2014; Kesavan et al., 2022). Van Yperen and Hagedoorn (2003) show that higher levels of job autonomy and control over work schedules significantly reduce the negative impact of high job demands on fatigue among nurses. A series of workplace intervention studies consistently show that providing employees with schedule control is the key to improving employees’ health, including burnout, depression, sleep, and work-life conflicts (Kelly et al., 2011; Moen et al., 2011, 2016).

However, the broader literature suggests that the effects of flexible work policies are contingent upon other factors. Empirical evidence regarding the ability of flexible work practices to mitigate burnout and improve employee outcomes has been mixed (see Kelly et al., 2008, for review). This inconsistency is partly because strong ideal worker norms that stigmatize employees who use these policies lead to underutilization of these resources (Blair-Loy, 2003; Cha and Grady, 2024; Munsch, 2016; Schieman et al., 2009). Additionally, the financial cost incurred from using these policies—such as unpaid time off or paid leave with lower wage replacement rate—can further inhibit

their use (Thébaud and Pedulla, 2022). This implies that for these policies to achieve their intended outcomes of preventing burnout and improving employee well-being, they must be accompanied by additional organizational support.

Some research finds that the efficacy of these policies is selective. Koltai and Schieman (2015) show that for workers with low socioeconomic status (SES), job autonomy and schedule flexibility help mitigate the effect of job pressure on anxiety. For those with high SES, by contrast, greater autonomy and schedule flexibility exacerbate this relationship. Related research introduces the concept of “stress of higher status” to explain this paradoxical pattern (e.g., Schieman et al., 2009). These scholars argue that workers in higher-status jobs face stronger normative pressure to demonstrate high-level work commitment, which serves as a unique stressor in these higher-status jobs. This, in turn, leads to a paradoxical pattern in which workers in higher-status jobs experience higher-level work permeability, despite having access to more job resources that could reduce burnout, such as greater temporal flexibility and control over their work activities.

In the context of STEMM jobs, these offsetting relationships between job demands and resources provide a useful framework for understanding burnout risks. Many STEMM jobs are characterized by long work hours, high time pressure, and large job demands. At the same time, however, they have greater access to job resources that could buffer the impact of these risk factors, such as higher job and financial security, more predictable work schedules, and better work-family policies (Blair-Loy and Cech, 2022; Cha and Grady, 2024; Schieman et al., 2009). However, the stronger ideal worker norms therein often inhibit the utilization of these resources. This implies that providing resources while minimizing the stigma and creating an organizational culture that promotes the utilization of these resources is a key to preventing burnout among workers in STEMM fields.

MICRO AND INTERACTIONAL-LEVEL: WORK IDENTITY, CULTURAL FIT, INTERACTIONAL INEQUALITY, EMOTIONAL LABOR

Work Identity and Meaningful Work

Strong identification with work can shield workers from burnout. A study of 555 nurses shows that while high job demands were associated with fatigue from work, these nurses also had high-level intrinsic motivation.

Other workers in high-status jobs, such as corporate executives, engineers, and investment bankers, similarly view work as their “calling,” “passion project,” and what makes their life “worthwhile” (Blair-Loy, 2003; E. Cech, 2021). This work identity and intrinsic motivation shield these workers from burnout. Many women executives in Blair-Loy (2003) expressed challenges of fast-paced and highly competitive jobs, but they were also strongly identified with and inspired by work. One of her interviewees expressed, “This profession gives me, in a lot of ways, a real piece of me, and the longer you do it, the more it gives you. . . . It’s been enormously good for me and not just financially. I mean, in terms of who I think and I know I am” (p. 11). Similarly, Dill et al. (2016) show that acute care hospital nurses who express high intrinsic (e.g., following their passion) and extrinsic motivation (e.g., for career success) show better health and job outcomes. They are also less likely to experience burnout and are less likely to leave their jobs, compared with those who have prosocial motivation (i.e., desire to help others).

The “meaningful” work serves as an important moderator in burnout-producing processes. Based on a meta-analysis of 55 studies, Crawford et al. (2010) show that not all high job demands lead to burnout, and instead, only job demands accompanied by tasks conflicting with their identities, such as role conflicts, role ambiguity, and organizational politics, are negatively associated with job engagement. By contrast, job demands involving tasks that individuals view as meaningful and important to them do not result in job disengagement. Similarly, in a study of employees from 21 European countries, De Moortel et al. (2017) show that not all long hours lead to negative health outcomes among men, but instead, only “involuntary” long hours—additional hours that employees were forced to put in—are negatively associated with their well-being outcomes.

However, Bredehorst et al. (2024) find that managing passion could also be a challenge. Using 30 days of daily diaries of individuals, the authors find that higher passion on one day is associated with higher emotional exhaustion the next day. Cech (2021) also warns that employees’ inclination to view their efforts to meet the job demands as a reflection of their passion can be an exploitation logic, in which employees “voluntarily” put in long hours for the benefit of employers, often at the expense of employees’ own personal well-being and health.

While some studies view work identification as a predictor for burnout outcomes (Blair-Loy, 2003; Cech, 2021; Cech et al., 2011), other scholars view strong work identification as an outcome of good working conditions. In a study of women finance executives, Blair-Loy (2003) argues that a

strong sense of dedication to jobs, employers, and professions is intertwined with reward systems. That is, employees who demonstrate complete allegiance are rewarded with large salary increases, promotions, and larger responsibilities that require longer work hours and more emotional energy to jobs. Focusing on these goals gives these workers a “rush of adrenaline” (p. 30) and “a sense of transcendence” (p. 32), which shields them from burnout. Simply put, economic reward systems and cultural recognition of work can also create a sense of strong work identification. Kalleberg (2011) similarly argues that favorable job conditions—higher earnings, generous employee benefits, job autonomy, and schedule flexibilities—can mold what we consider “intrinsic rewards,” one’s subjective experiences as meaningful and interesting jobs. By the same logic, poor working conditions can make workers lose their strong sense of commitment to work. Yavaş (2024) shows that overloaded job demands, lacking work-life balance, and feeling alienated at work led high-power elite white-collar employees to ask an existential question, “What am I doing with my life?” (p. 762), which ultimately led them to leave their jobs.

Cultural Fit

A strong identification with work can be influenced by cultural beliefs about stereotypical traits and skills associated with ideal employees in STEMM fields. Research has shown that while an increasing number of women select STEMM majors, women are less likely than men to pursue careers relevant to their majors. Using the National Science Foundation’s Scientists and Engineers Statistical Data System, Sassler et al. (2023) show that among computer science degree holders, 66 percent of men with computer science degrees have jobs in STEM, but only 52 percent of women do.

Several studies offer explanations for this “gendered persistence” in STEMM careers. Based on longitudinal surveys of engineering students from four colleges, E. Cech et al. (2011) show that female students are more likely than men to believe that they lack expertise and career fit to pursue engineering careers. This lower “professional role confidence” is a strong predictor of women’s lower rate in pursuing engineering careers. In a follow-up study, the same authors show that women’s lower professional confidence often results from professional socialization in college engineering programs (Seron et al., 2016). During orientation, coursework and team projects, and internships, women frequently encounter stereotypes, are relegated to

supporting roles, and receive fewer opportunities to develop technical skills. This gendered socialization in turn contributes to their lower “professional role confidence” as engineers.

Studies find that employees’ self-assessed fit with their jobs and professions impacts their job satisfaction and their persistence. In a study of technical employees across seven Silicon Valley firms, Wynn and Correll (2017) show that women are less likely than men to perceive themselves as fitting the image of successful tech employees. The authors also show that this self-assessed “cultural” and “skill” alignment is a strong predictor of their intention to leave. In another study, these authors observed that during recruiting sessions, company representatives use images, languages, and behaviors that reinforce gender stereotypes, exhibiting men presenters taking the technical roles while women presenters take supporting roles, and often emphasizing masculine geek cultures (Wynn and Correll, 2018). The authors argue that these recruitment strategies work as gendered cultural signals about who belongs in the tech industry, further alienating women.

A separate stream of the literature examines how beliefs on the role of beliefs in meritocracy and objectivity of STEMM can undermine efforts to promote equality (Blair-Loy and Cech, 2022; Doerr et al., 2021). Blair-Loy and Cech (2022) demonstrate that strong beliefs in objectivity in science and engineering often prevent STEM faculty from recognizing unequal outcomes and structural barriers faced by women and minoritized scholars. This phenomenon is well-documented elsewhere, illustrated by the concept “paradox of meritocracy” (Castilla and Benard, 2010): a phenomenon that when individuals perceive the system as meritocratic, they are less vigilant about the effects of institutionalized biases, and consequently, become more prone to those biases than individuals who believe that the system is unjust.

Moreover, the concepts of “scientific excellence” and “merit” may seem “objective,” but these ideals are constructed around the values and attributes of White heterosexual men (e.g., assertiveness, self-promotion), thereby perpetuating cultures and norms that disadvantage women and racial and sexual minority workers. The belief in meritocracy is also widely accepted by women and minoritized STEM workers, influencing their self-assessed career fit. Doerr et al. (2021) show that despite experiencing marginalization in their engineering careers, women engineers from diverse ethnoracial backgrounds often endorse the idea that gender is irrelevant in engineering and the field operates on meritocratic principles.

Related literature shows how the “abstract” and disembodied notion of the worker, constructed around White heterosexual men, their bodies, and their lifestyles (Acker, 1990), creates structural disadvantages for women and minoritized workers in STEMM. Women’s experiences tied to motherhood are largely hidden in the workplace and considered what women need to manage privately to maintain a “professional” appearance. In a study of graduate students in science and engineering programs, Thébaud and Taylor (2021) find that motherhood is broadly seen as undermining their professional legitimacy as scientists and engineers. In her autoethnography, Haynes (2024) speaks about how managerialism is highly tied to masculine norms in academic leadership, and how the pressures assimilated to these norms lead many women academics to experience burnout.

Recent empirical studies document how women’s bodily experiences, such as pregnancy, lactation, and menopause, are often disregarded in the professional workspace and are considered something to hide, which can lead to feelings of exclusion, emotional exhaustion, perceived discrimination, and burnout (Little et al., 2015; Steffan and Loretto, 2024; Watts, 2009). Little et al. (2015) show nearly all professional women they interviewed reported concerns about their professional images from the early stages of their pregnancy. Many of these women used strategies of detaching themselves from pregnancy, such as keeping the pace of work and avoiding asking for special accommodations. The authors find that women who used these strategies reported significantly lower levels of burnout, perceived discrimination, and turnover rate than women who did not. This may appear good news at first glance, as it seems to suggest that individual strategies of “hiding” pregnancy are effective. However, the underlying implication is troubling, as the subtle implication is that women who did embrace their pregnant identity at work experienced significantly higher rates of burnout, perceived discrimination, and turnover. Simply put, without the individual effort to conceal their pregnancy, pregnant workers face a higher risk of burnout and marginalization at work.

Interactional Inequality

Studies find that workplace interaction is an important source of burnout (Beck et al., 2022; Cardador et al., 2022; W. Hall et al., 2019; W. M. Hall et al., 2015; Haynes, 2024; Maslach and Leiter, 2008; Nelson et al., 2023; Wingfield and Chavez, 2020). These scholars identify several

key mechanisms contributing to burnout in the international processes, including challenges of being a token (or a numerical minority), status beliefs and stereotypes, and discrimination.

In a classic study of women and men in a large corporation, Kanter (1977) argues that being a numerical minority brings numerous challenges for women in male-dominated workspaces. They include hypervisibility, increased scrutiny on their work performance, being seen through stereotypical lenses, and pressure to conform to men-centered sexist cultures. Subsequent work has linked these challenges to psychological well-being and job outcomes in broader contexts (Jackson et al., 1995; Taylor, 2016). Using a laboratory study, Taylor (2016) shows that being the only man or woman in a mixed-gender group elevates the stress hormone levels for both genders. Jackson et al. (1995) find that Black leaders across business, politics, and media in predominantly White workspaces experience higher-level stress, with heightened visibility identified as a major driver.

Another strand of research shows that women and minoritized workers experience disadvantages in small group interactions because they are widely perceived as “lower status” groups (Ridgeway, 2011, 2014). As members of a lower-status group, their work competency is questioned, and their contribution is often underappreciated, compared with their White men counterparts. The sense of being underappreciated has been linked to a host of burnout-related outcomes, including feelings of discouragement and injustice, lower job satisfaction, and decreased psychological well-being, work disengagement, and higher turnover rates (Beck et al., 2022; W. Hall et al., 2019; W. M. Hall et al., 2015; Haynes, 2024; Pascoe and Smart Richman, 2009).

The associations between perceived racial discrimination and psychological well-being outcomes are well established (King et al., 2023; Muñoz and Villanueva, 2022; Pascoe and Smart Richman, 2009). A study of 374 Black employees across the United States shows that experiencing microaggressions is positively associated with burnout (King et al., 2023). A review article on Latino/a faculty in STEM documents a clear pattern that those who experience discrimination experience negative psychological health outcomes (Muñoz and Villanueva, 2022). W. Hall et al. (2019) show these stressors are conveyed through everyday interactions. Based on a study of engineering workers in Canada and graduate students in STEM in North American universities, the authors show that having conversations with their male colleagues who display less acceptance of

women in the workplace are strongly associated with burnout among STEM women.

Emotional Labor

In her study of flight attendants and bill collectors, Hochschild (1983) introduced the concept of “emotional labor”—the management and display of emotions in the workplace. This concept inspired numerous studies to investigate the nature and consequences of such labor. While much of the work has been focused on service jobs, such as restaurant work, nail salon workers, retail clerks, and childcare workers (Godwyn, 2006; Kang, 2003; Macdonald and Sirianni, 1996), recent research shows that emotional labor is also a prominent aspect of high-skilled managerial and professional occupations (see Wharton, 2009, for a review).

Specifically, emotional labor has been used to understand the psychological well-being of healthcare workers (Cottingham and Erickson, 2020; Erickson and Grove, 2008). Cottingham and Erickson (2020) show that care nurses often experience a complex range of emotions, such as worry, anger, sadness, and frustration, due to the interactive nature of their jobs with patients. However, they are expected to suppress, manage, and display these emotions in limited ways. Data from 48 acute care hospital nurses show that the challenges of managing emotions arising from demanding interactions with doctors and patients led to physical and psychological symptoms, such as headaches, insomnia, and depression. Based on a survey of more than 3,000 women RNs (registered nurses) and nursing aids in France, Jolivet et al. (2010) found that poor interpersonal relationships between workers are associated with higher depressive scores among nurses. Stokar (2024) shows that negative patient outcomes give physicians and nurses a “sense of failure,” which increases the rate of burnout and traumatic stress.

Scholars also point to gendered and racialized “feeling rules,” which may increase the emotional strain among women and minoritized workers (R. Collins, 2004; Pierce, 1996; Wingfield, 2010, 2021). In a study of legal professionals, Pierce (1996) shows that while litigators are expected to use anger and aggressiveness as litigation tactics, these behaviors are often viewed more negatively when exhibited by women attorneys. This different feeling rules can create additional cognitive labor. Harlow (2003) shows that Black professors engage in additional emotional work during interactions with students to navigate through negative racial stereotypes of being less intelligent.

Given that technical expertise required in STEMM fields are less aligned with traits that women and Black and Hispanic workers are stereotypically viewed to possess, the emotional and cognitive labor fighting for stereotypes while expressing emotions can make their work life doubly challenging.

IMPLICATIONS FOR GENDER AND RACE DISPARITIES

So far, we have reviewed academic research that identifies burnout risk factors at macro, meso, and micro levels. In this section, we revisit key findings to understand how these factors reinforce gender and race disparities in STEMM. We offer a conceptual tool to categorize these factors into two categories: (1) “disparate” risk factors and (2) gender- and race-specific risk factors.

First, some factors we reviewed appear to be gender- and race-neutral at first glance. Many of the poor working conditions (e.g., long hours, lack of autonomy, schedule control) are good examples of this, as they affect whoever is exposed to it, regardless of gender and race. However, when combined with persistent gender and race segregation in jobs—women and racial minorities disproportionately work in lower-skilled, lower-quality, and lower-paying jobs, compared with White men—these seemingly gender- and race-neutral factors produce gender- and race-specific patterns, in which more women and minoritized workers are exposed to these conditions. For example, emotional labor is more intense among nurses than among doctors, while a higher proportion of women is found among nurses than doctors. Consequently, more women than men are exposed to emotional labor.

Higher-status occupations and higher-performing organizations offer more job resources (e.g., job autonomy, schedule flexibilities) that help to prevent burnout (Davis and Kalleberg, 2006). And women and minoritized workers are underrepresented in these occupations and organizations (Glass, 2004; Hodges, 2020). By analyzing texts from 155 work groups appearing in 162 published ethnographies, Crowley (2012) shows that work groups consisting of predominantly men were designed to offer higher-level autonomy, creativity, meaningfulness, and satisfaction. By contrast, work groups consisting of predominantly women were run with more coercive arrangement, using more direct supervision rather than offering autonomy.

Second, some factors more directly speak to gendered and racialized processes that create systematic disadvantages for women and minoritized workers. These factors include race and gender biases that operate at the

micro level through everyday interactions and are institutionalized as norms and practices at the meso level. This strand of research demonstrates that the gendered notion of the ideal workers and gendered and racialized cultural ideals of the STEMM workers leads to a lack of cultural fit for women and minoritized workers. Interactional inequalities based on stereotypes lead to underappreciation of the contribution made by women and minoritized workers and raise doubts about their work competency. These processes are strong predictors of gender-specific and race-specific burnout factors.

SUMMARY AND CONCLUSION

In the contemporary workplace, an increasing number of people report exhaustion from work, time pressure, and work-family conflicts. These phenomena have been linked to poorer physical and psychological health, lower job satisfaction, and higher turnover rates. In this paper, we review academic research that identifies sources of burnout in STEMM fields. In so doing, we argue that burnout is an outcome produced through multilevel processes at macro (macroeconomic, cultural, and demographic changes), meso (workplace and job factors), and micro (identities and interactional inequality) levels and review relevant studies in this organizing frame.

First, we began by introducing macro-level changes, which created structural conditions making everyone in the economy prone to burnout. From this perspective, the rise of burnout is an outcome of macrostructural shifts that occurred in the U.S. economy over the last half-century. We discussed how increased global competition and market pressures have increased job demands for high-skilled workers, including STEMM workers, while decreasing job insecurity and working conditions for low-skilled workers. These economic changes were also accompanied by cultural shifts that have intensified work and parenting norms. The rise in workplace expectations for around-the-clock availability, facilitated by mobile communication technologies, has increased the spillover of work into family life. Concurrently, the emergence of intensive parenting norms, which require greater effort and time commitment to family care, has led many employees to experience heightened work-family conflicts. Demographic shifts toward dual-earner households have dramatically raised work hours at the family level than at the individual level.

Second, much of the research literature on burnout focuses on workplace and job factors that have become more pronounced because of macro-level changes. Most notably, increased job demands have been

identified as a major source of burnout among high-skilled workers. That said, the impact of job demands is contingent upon job resources available to individual workers. When workers have more control over work activities and schedules, and receive better support from management and coworkers, the negative effects of job demands can be mitigated. Similarly, tasks that are less routinized and require higher-level creativity can also help alleviate the adverse effects of high job demands among high-skilled workers, such as STEMM workers.

Third, at the micro level, identities and interactions play important roles in creating or reducing mental exhaustion at work. Gender and race biases result in interactional disadvantages for women and minority workers: these workers are seen as less competent and suitable for STEMM fields. This creates feelings of discouragement and injustice and diminishes their sense of belonging. An emerging body of literature examines the embodied nature of work, particularly institutionalized practices and norms that implicitly or explicitly assume White heterosexual masculinity. Workplace practices and norms built upon the notion that the ideal type of workers embodies the characteristics, cultural values, and lifestyles of White heterosexual men can disadvantage workers who do not fit this mold. This perceived lack of fit can in turn increase burnout.

Many STEMM workers are employed in high-performing well-resourced organizations. Their work lives are centered on competitive, fast-paced, and overloaded job demands. Despite this, STEMM workers are generally part of relatively privileged groups, who enjoy higher earnings, job security, and job resources that can help buffer the impact of higher job demands. However, a prevailing occupational norm that valorizes “overwork,” and stigmatizes workers who seek work-life balance or utilize organizational resources to achieve it can put these workers at a higher risk of burnout. Occupational values emphasizing “objectivity” and “scientific expertise” can also hinder efforts to address deeply institutionalized gender- and race-based biases within STEMM fields. In our view, these more covert forms of bias, which are hidden in STEMM education, curriculum, cultures, and compensation systems, are less explored in the literature, which are promising avenues for future research.

The 2020 COVID-19 pandemic brought several important changes in individuals’ work life. Many people worked from home during this time, and many workers wish to keep such flexibility in the post-pandemic years (Parker et al., 2022). Some scholars suspect that the pandemic experience has also reshaped employees’ relationship with work. A record

number of people left jobs (the Great Resignation), and some have become disengaged from work without quitting entirely (quiet quitting). The alarming signs of burnout during the pandemic have elevated mental health and well-being concerns to a national priority (U.S. Department of Health and Human Services, 2024). How will these changes impact STEMM workers in the post-pandemic era? While we have gained cultural momentum in raising awareness of burnout as a social issue, our reviews suggest that structural changes in work and a shift away from the toxic ideal worker norms are essential to fundamentally address the problem in STEMM and beyond.

REFERENCES

Acker, J. (1990). Hierarchies, Jobs, Bodies: A Theory of Gendered Organizations. Gender & Society, 4(2), 139–158. https://doi.org/10.1177/089124390004002002.

Ahola, K., Honkonen, T., Isometsä, E., Kalimo, R., Nykyri, E., Aromaa, A., & Lönnqvist, J. (2005). The Relationship between Job-Related Burnout and Depressive Disorders—Results from the Finnish Health 2000 Study. Journal of Affective Disorders, 88(1), 55–62. https://doi.org/10.1016/j.jad.2005.06.004.

Aiken, L. H., Clarke, S. P., Sloane, D. M., Sochalski, J., & Silber, J. H. (2002). Hospital Nurse Staffing and Patient Mortality, Nurse Burnout, and Job Dissatisfaction. JAMA, 288(16), 1987–1993. https://doi.org/10.1001/jama.288.16.1987.

Aldossari, M., & Chaudhry, S. (2021). Women and Burnout in the Context of a Pandemic. Gender, Work & Organization, 28(2), 826–834. https://doi.org/10.1111/gwao.12567.

American Psychological Association. (2023). 2023 Work in America Survey: Workplaces as Engines of Psychological Health and Well-Being. https://www.apa.org/pubs/reports/work-in-america/2023-workplace-health-well-being.

Bakker, A. B., & Demerouti, E. (2007). The Job Demands-Resources Model: State of the Art. Journal of Managerial Psychology, 22(3), 309–328. https://doi.org/10.1108/02683940710733115.

Barley, S. R., Meyerson, D. E., & Grodal, S. (2011). E-mail as a Source and Symbol of Stress. Organization Science, 22(4), 887–906. https://doi.org/10.1287/orsc.1100.0573.

Beck, M., Cadwell, J., Kern, A., Wu, K., Dickerson, M., & Howard, M. (2022). Critical Feminist Analysis of STEM Mentoring Programs: A Meta-Synthesis of the Existing Literature. Gender, Work & Organization, 29(1), 167–187. https://doi.org/10.1111/gwao.12729.

Beckman, C. M., & Mazmanian, M. (2020). Dreams of the Overworked: Living, Working, and Parenting in the Digital Age. Stanford University Press. http://ebookcentral.proquest.com/lib/iub-ebooks/detail.action?docID=6173725.

Bianchi, S. M., Casper, L. M., & King, R. B. (2006). Work, Family, Health, and Well-Being. Routledge.

Bianchi, S. M., Sayer, L. C., Milkie, M. A., & Robinson, J. P. (2012). Housework: Who Did, Does or Will Do It, and How Much Does It Matter? Social Forces, 91(1), 55–63. https://doi.org/10.1093/sf/sos120.

Biggart, L., & O’Brien, M. (2010). Uk Fathers’ Long Work Hours: Career Stage or Fatherhood? Fathering, 8(3), 341–361. https://doi.org/10.3149/fth.0803.341.

Blair-Loy, M. (2003). Competing Devotions: Career and Family among Women Executives. Harvard University Press. https://doi.org/10.4159/9780674021594.

Blair-Loy, M., & Cech, E. A. (2022). Misconceiving Merit. University of Chicago Press.

Borritz, M., Christensen, K. B., Bültmann, U., Rugulies, R., Lund, T., Andersen, I., Villadsen, E., Diderichsen, F., & Kristensen, T. S. (2010). Impact of Burnout and Psychosocial Work Characteristics on Future Long-Term Sickness Absence. Prospective Results of the Danish PUMA Study Among Human Service Workers. Journal of Occupational and Environmental Medicine, 52(10), 964. https://doi.org/10.1097/JOM.0b013e3181f12f95.

Bredehorst, J., Krautter, K., Meuris, J., & Jachimowicz, J. M. (2024). The Challenge of Maintaining Passion for Work over Time: A Daily Perspective on Passion and Emotional Exhaustion. Organization Science, 35(1), 364–386. https://doi.org/10.1287/orsc.2023.1673.

Bruns & Lingo (2024). Tedious Work: Developing Novel Outcomes with Digitization in the Arts and Sciences. Administrative Science Quarterly, 69(1), 39–79. https://doi.org/10.1177/00018392231208190

Calarco, J. (2024). Holding It Together. Portfolio/Penguin. https://www.penguinrandomhouse.com/books/697130/holding-it-together-by-jessica-calarco/.

Cardador, M. T., Hill, P. L., & Salles, A. (2022). Unpacking the Status-Leveling Burden for Women in Male-Dominated Occupations. Administrative Science Quarterly, 67(1), 237–284. https://doi.org/10.1177/00018392211038505.

Castilla, E. J., & Benard, S. (2010). The Paradox of Meritocracy in Organizations. Administrative Science Quarterly, 55(4), 543–676. https://doi.org/10.2189/asqu.2010.55.4.543.

Cech, E. (2021). The Trouble with Passion: How Searching for Fulfillment at Work Fosters Inequality. University of California Press.

Cech, E. A., & Blair-Loy, M. (2014). Consequences of Flexibility Stigma Among Academic Scientists and Engineers. Work and Occupations, 41(1), 86–110. https://doi.org/10.1177/0730888413515497.

Cech, E., Rubineau, B., Silbey, S., & Seron, C. (2011). Professional Role Confidence and Gendered Persistence in Engineering. American Sociological Review, 76(5), 641–666. https://doi.org/10.1177/0003122411420815.

Cha, Y. (2010). Reinforcing Separate Spheres: The Effect of Spousal Overwork on Men’s and Women’s Employment in Dual-Earner Households. American Sociological Review, 75(2), 303–329. https://doi.org/10.1177/0003122410365307.

Cha, Y., & Grady, R. K. (2024). Overwork and the Use of Paid Leave and Flexible Work Policies in U.S. Workplaces. Social Science Research, 121, 103006. https://doi.org/10.1016/j.ssresearch.2024.103006.

Cha, Y., & Weeden, K. A. (2014). Overwork and the Slow Convergence in the Gender Gap in Wages. American Sociological Review, 79(3), 457–484. https://doi.org/10.1177/0003122414528936.

Collins, C. (2019). Making Motherhood Work: How Women Manage Careers and Caregiving. Princeton University Press. https://doi.org/10.1515/9780691185156.

Collins, R. (2004). Rituals of Solidarity and Security in the Wake of Terrorist Attack. Sociological Theory, 53–86.

Coser, L. A. (1967). Greedy Organisations. European Journal of Sociology / Archives Européennes de Sociologie, 8(2), 196–215. https://doi.org/10.1017/S000397560000151X.

Cottingham, M. D., & Erickson, R. J. (2020). The Promise of Emotion Practice: At the Bedside and Beyond. Work and Occupations, 47(2), 173–199. https://doi.org/10.1177/0730888419892664.

Crawford, E. R., LePine, J. A., & Rich, B. L. (2010). Linking Job Demands and Resources to Employee Engagement and Burnout: A Theoretical Extension and Meta-Analytic Test. Journal of Applied Psychology, 95(5), 834–848. https://doi.org/10.1037/a0019364.

Crowley, M. (2012). Gender, the Labor Process and Dignity at Work Gender Inequality. Social Forces, 91(4), 1209–1238.

Daminger, A. (2019). The Cognitive Dimension of Household Labor. American Sociological Review, 84(4), 609–633. https://doi.org/10.1177/0003122419859007.

Davis, A. E., & Kalleberg, A. L. (2006). Family-Friendly Organizations? Work and Family Programs in the 1990s. Work and Occupations, 33(2), 191–223. https://doi.org/10.1177/0730888405280446.

De Moortel, D., Thévenon, O., De Witte, H., & Vanroelen, C. (2017). Working Hours Mismatch, Macroeconomic Changes, and Mental Well-being in Europe. Journal of Health and Social Behavior, 58(2), 217–231. https://doi.org/10.1177/0022146517706532.

Dill, J., Erickson, R. J., & Diefendorff, J. M. (2016). Motivation in Caring Labor: Implications for the Well-Being and Employment Outcomes of Nurses. Social Science & Medicine, 167, 99–106. https://doi.org/10.1016/j.socscimed.2016.07.028.

Doerr, K., Riegle-Crumb, C., Russo-Tait, T., Takasaki, K., Sassler, S., & Levitte, Y. (2021). Making Merit Work at the Entrance to the Engineering Workforce: Examining Women’s Experiences and Variations by Race/Ethnicity. Sex Roles, 85(7), 422–439. https://doi.org/10.1007/s11199-021-01233-6,

Ducharme, L. J., Knudsen, H. K., & Roman, P. M. (2007). Emotional Exhaustion and Turnover Intention in Human Service Occupations: The Protective Role of Coworker Support. Sociological Spectrum, 28(1), 81–104. https://doi.org/10.1080/02732170701675268.

Ducharme, L. J., & Martin, J. K. (2000). Unrewarding Work, Coworker Support, and Job Satisfaction: A Test of the Buffering Hypothesis. Work and Occupations, 27(2), 223–243. https://doi.org/10.1177/0730888400027002005.

Duffee, B., & Willis, D. B. (2023). Paramedic Perspectives of Job Stress: Qualitative Analysis of High-Stress, High-Stakes Emergency Medical Situations. Social Science & Medicine, 333, 116177. https://doi.org/10.1016/j.socscimed.2023.116177.

Epstein, C. F., Seron, C., Oglensky, B., & Sauté, R. (1999). The Part-time Paradox: Time Norms, Professional Life, Family and Gender. Routledge. https://doi.org/10.4324/9781315811352.

Erickson, R. J., & Grove, W. J. C. (2008). Why Emotions Matter: Age, Agitation, and Burnout Among Registered Nurses. Online Journal of Issues in Nursing, 13(1), 1–12. https://doi.org/10.3912/OJIN.Vol13No01PPT01.

Fligstein, N., & Shin, T.-J. (2004). The Shareholder Value Society: A Review of the Changes in Working Conditions and Inequality in the United States, 1976 to 2000. In Social Inequality (pp. 401–432). Russell Sage Foundation.

Gerstel, N., & Clawson, D. (2014). Unequal Time: Gender, Class, and Family in Employment Schedules. Russell Sage Foundation. https://muse-jhu-edu.proxyiub.uits.iu.edu/pub/207/monograph/book/33240.

Glass, J. (2004). Blessing or Curse?: Work-Family Policies and Mother’s Wage Growth Over Time. Work and Occupations, 31(3), 367–394. https://doi.org/10.1177/0730888404266364.

Glavin, P., & Schieman, S. (2022). Dependency and Hardship in the Gig Economy: The Mental Health Consequences of Platform Work. Socius, 8, 23780231221082414. https://doi.org/10.1177/23780231221082414.

Glavin, P., Schieman, S., & Reid, S. (2011). Boundary-Spanning Work Demands and Their Consequences for Guilt and Psychological Distress. Journal of Health and Social Behavior, 52(1), 43–57. https://doi.org/10.1177/0022146510395023.

Godwyn, M. (2006). Using Emotional Labor to Create and Maintain Relationships in Service Interactions. Symbolic Interaction, 29(4), 487–506. https://doi.org/10.1525/si.2006.29.4.487.

Guarino, C. M., & Borden, V. M. H. (2017). Faculty Service Loads and Gender: Are Women Taking Care of the Academic Family? Research in Higher Education, 58(6), 672–694. https://doi.org/10.1007/s11162-017-9454-2.

Hall, W. M., Schmader, T., & Croft, E. (2015). Engineering Exchanges: Daily Social Identity Threat Predicts Burnout Among Female Engineers. Social Psychological and Personality Science, 6(5), 528–534. https://doi.org/10.1177/1948550615572637.

Hall, W., Schmader, T., Aday, A., & Croft, E. (2019). Decoding the Dynamics of Social Identity Threat in the Workplace: A Within-Person Analysis of Women’s and Men’s Interactions in STEM. Social Psychological and Personality Science, 10(4), 542–552. https://doi.org/10.1177/1948550618772582.

Harlow, R. (2003). “Race Doesn’t Matter, but...”: The Effect of Race on Professors’ Experiences and Emotion Management in the Undergraduate College Classroom. Social Psychology Quarterly, 66(4), 348–363. https://doi.org/10.2307/1519834.

Haynes, K. (2024). Resisting Sexisms, Aggression, and Burnout in Academic Leadership: Surviving in the Gendered Managerial Academy. Gender, Work & Organization, 1–17. https://doi.org/10.1111/gwao.13137.

Hays, S. (1996). The Cultural Contradictions of Motherhood. Yale University Press.

Hochschild, A. (1983). The Managed Heart. University of California Press. https://www.ucpress.edu/books/the-managed-heart/paper.

Hochschild, A., & Machung, A. (1989). The Second Shift: Working Families and the Revolution at Home. Penguin.

Hodges, L. (2020). Do Female Occupations Pay Less but Offer More Benefits? Gender & Society, 34(3), 381–412. https://doi.org/10.1177/0891243220913527.

Hutter, M. M., Kellogg, K. C., Ferguson, C. M., Abbott, W. M., & Warshaw, A. L. (2006). The Impact of the 80-Hour Resident Workweek on Surgical Residents and Attending Surgeons: Annals of Surgery, 243(6), 864–875. https://doi.org/10.1097/01.sla.0000220042.48310.66.

Ishizuka, P. (2019). Social Class, Gender, and Contemporary Parenting Standards in the United States: Evidence from a National Survey Experiment. Social Forces, 98(1), 31–58. https://doi.org/10.1093/sf/soy107.

Jackson, P. B., Thoits, P. A., & Taylor, H. F. (1995). Composition of the Workplace and Psychological Well-Being: The Effects of Tokenism on America’s Black Elite*. Social Forces, 74(2), 543–557. https://doi.org/10.1093/sf/74.2.543.

Jacobs, J. A., & Gerson, K. (2004). Understanding Changes in American Working Time: A Synthesis. In Fighting for Time: Shifting Boundaries of Work and Social Life. Russell Sage Foundation.

Jacobs, J. A., & Winslow, S. E. (2004). Overworked Faculty: Job Stresses and Family Demands. The ANNALS of the American Academy of Political and Social Science, 596(1), 104–129. https://doi.org/10.1177/0002716204268185.

Jolivet, A., Caroly, S., Ehlinger, V., Kelly-Irving, M., Delpierre, C., Balducci, F., Sobaszek, A., De Gaudemaris, R., & Lang, T. (2010). Linking Hospital Workers’ Organisational Work Environment to Depressive Symptoms: A Mediating Effect of Effort–Reward Imbalance? The ORSOSA Study. Social Science & Medicine, 71(3), 534–540. https://doi.org/10.1016/j.socscimed.2010.04.003.

Judiesch, M. K., & Lyness, K. S. (1999). Left Behind? The Impact of Leaves of Absence on Managers’ Career Success. Academy of Management Journal, 42(6), 641–651.

Kaduk, A., Genadek, K., Kelly, E. L., & Moen, P. (2019). Involuntary vs. Voluntary Flexible Work: Insights for Scholars and Stakeholders. Community, Work & Family, 22(4), 412–442. https://doi.org/10.1080/13668803.2019.1616532.

Kalleberg, A. L. (2001). Organizing Flexibility: The Flexible Firm in a New Century. British Journal of Industrial Relations, 39(4), 479–504. https://doi.org/10.1111/1467-8543.00211.

Kalleberg, A. L. (2011). Good Jobs, Bad Jobs: The Rise of Polarized and Precarious Employment Systems in the United States, 1970s-2000s. Russell Sage Foundation.

Kalleberg, A. L. (2016). Good Jobs, Bad Jobs. In The SAGE Handbook of the Sociology of Work and Employment (pp. 111–128). SAGE Publications, Limited. http://ebookcentral.proquest.com/lib/iub-ebooks/detail.action?docID=4531683.

Kang, M. (2003). The Managed Hand: The Commercialization of Bodies and Emotions in Korean Immigrant–Owned Nail Salons. Gender & Society, 17(6), 820–839. https://doi.org/10.1177/0891243203257632.

Kanter, R. M. (1977). Some Effects of Proportions on Group Life: Skewed Sex Ratios and Responses to Token Women. American Journal of Sociology, 82(5), 965–990. https://doi.org/10.1086/226425.

Karasek, R. A. (1979). Job Demands, Job Decision Latitude, and Mental Strain: Implications for Job Redesign. Administrative Science Quarterly, 24(2), 285–308. https://doi.org/10.2307/2392498.

Kellogg, K. C. (2009). Operating Room: Relational Spaces and Microinstitutional Change in Surgery. American Journal of Sociology, 115(3), 657–711. https://doi.org/10.1086/603535.

Kelly, E. L., Ammons, S. K., Chermack, K., & Moen, P. (2010). Gendered Challenge, Gendered Response: Confronting the Ideal Worker Norm in a White-Collar Organization. Gender & Society, 24(3), 281–303. https://doi.org/10.1177/0891243210372073.

Kelly, E. L., Kossek, E. E., Hammer, L. B., Durham, M., Bray, J., Chermack, K., Murphy, L. A., & Kaskubar, D. (2008). Getting There from Here: Research on the Effects of Work–Family Initiatives on Work–Family Conflict and Business Outcomes. Academy of Management Annals, 2(1), 305–349. https://doi.org/10.5465/19416520802211610.

Kelly, E. L., Moen, P., Oakes, J. M., Fan, W., Okechukwu, C., Davis, K. D., Hammer, L. B., Kossek, E. E., King, R. B., Hanson, G. C., Mierzwa, F., & Casper, L. M. (2014). Changing Work and Work-Family Conflict: Evidence from the Work, Family, and Health Network. American Sociological Review, 79(3), 485–516. https://doi.org/10.1177/0003122414531435.

Kelly, E. L., Moen, P., & Tranby, E. (2011). Changing Workplaces to Reduce Work-Family Conflict: Schedule Control in a White-Collar Organization. American Sociological Review, 76(2), 265–290. https://doi.org/10.1177/0003122411400056.

Kesavan, S., Lambert, S. J., Williams, J. C., & Pendem, P. K. (2022). Doing Well by Doing Good: Improving Retail Store Performance with Responsible Scheduling Practices at the Gap, Inc. Management Science, 68(11), 7818–7836. https://doi.org/10.1287/mnsc.2021.4291.

King, D. D., Fattoracci, E. S. M., Hollingsworth, D. W., Stahr, E., & Nelson, M. (2023). When Thriving Requires Effortful Surviving: Delineating Manifestations and Resource Expenditure Outcomes of Microaggressions for Black Employees. Journal of Applied Psychology, 108(2), 183–207. https://doi.org/10.1037/apl0001016.

Kleiner, S., & Pavalko, E. K. (2010). Clocking In: The Organization of Work Time and Health in the United States. Social Forces, 88(3), 1463–1486. https://doi.org/10.1353/sof.0.0301.

Kleiner, S., Schunck, R., & Schömann, K. (2015). Different Contexts, Different Effects?: Work Time and Mental Health in the United States and Germany. Journal of Health and Social Behavior, 56(1), 98–113. https://doi.org/10.1177/0022146514568348.

Koltai, J., & Schieman, S. (2015). Job Pressure and SES-contingent Buffering: Resource Reinforcement, Substitution, or the Stress of Higher Status? Journal of Health and Social Behavior, 56(2), 180–198. https://doi.org/10.1177/0022146515584151.

Kossek, E. E., Lautsch, B. A., & Eaton, S. C. (2006). Telecommuting, Control, and Boundary Management: Correlates of Policy Use and Practice, Job Control, and Work–Family Effectiveness. Journal of Vocational Behavior, 68(2), 347–367. https://doi.org/10.1016/j.jvb.2005.07.002.

Kossek, E. E., & Ozeki, C. (1999). Bridging the Work—Family Policy and Productivity Gap: A Literature Review. Community, Work & Family, 2(1), 7–32. https://doi.org/10.1080/13668809908414247.

Kossek, E. E., Rosokha, L. M., & Leana, C. (2020). Work Schedule Patching in Health Care: Exploring Implementation Approaches. Work and Occupations, 47(2), 228–261. https://doi.org/10.1177/0730888419841101.

Kuhn, P., & Lozano, F. (2008). The Expanding Workweek? Understanding Trends in Long Work Hours among U.S. Men, 1979–2006. Journal of Labor Economics, 26(2), 311–343. https://doi.org/10.1086/533618.

Kunda, G., Barley, S. R., & Evans, J. (2002). Why Do Contractors Contract? The Experience of Highly Skilled Technical Professionals in a Contingent Labor Market. ILR Review, 55(2), 234–261. https://doi.org/10.1177/001979390205500203.

Landers, R. M., Rebitzer, J. B., & Taylor, L. J. (1996). Rat Race Redux: Adverse Selection in the Determination of Work Hours in Law Firms. The American Economic Review, 86(3), 329–348.

Lareau, A. (2003). Unequal Childhoods: Class, Race, and Family Life. In Inequality in the 21st Century. Routledge.

Lebel, R. D., Yang, X., Parker, S. K., & Kamran-Morley, D. (2023). What Makes You Proactive Can Burn You Out: The Downside of Proactive Skill Building Motivated by Financial Precarity and Fear. Journal of Applied Psychology, 108(7), 1207–1222. https://doi.org/10.1037/apl0001063.

Lee, J. J., Kelley, K., Mead, C., & Cha, Y. (2024). ‘It’s Just My Personality.’ How Employees Make Sense of Their Long Work Hours in a Supportive Workplace. Community, Work & Family, 1–21. https://doi.org/10.1080/13668803.2024.2396600.

Little, L. M., Smith Major, V., Hinojosa, A. S., & Nelson, D. L. (2015). Professional Image Maintenance: How Women Navigate Pregnancy in the Workplace. Academy of Management Journal, 58(1), 8–37. https://doi.org/10.5465/amj.2013.0599.

Macdonald, C. L., & Sirianni, C. (1996). Working in the Service Society. Temple University Press.

Marchand, A., Blanc, M.-E., & Beauregard, N. (2018). Do Age and Gender Contribute to Workers’ Burnout Symptoms? Occupational Medicine, 68(6), 405–411. https://doi.org/10.1093/occmed/kqy088.

Maslach, C., & Leiter, M. P. (2008). Early Predictors of Job Burnout and Engagement. Journal of Applied Psychology, 93(3), 498–512. https://doi.org/10.1037/0021-9010.93.3.498.

Maslach, C., Schaufeli, W. B., & Leiter, M. P. (2001). Job Burnout. Annual Review of Psychology, 52, 397–422. https://doi.org/10.1146/annurev.psych.52.1.397.

Matos, K., & Galinsky, E. (2011). Workplace Flexibility in the United States: A Status Report. Families and Work Institute.

Melamed, S., Shirom, A., Toker, S., Berliner, S., & Shapira, I. (2006). Burnout and Risk of Cardiovascular Disease: Evidence, Possible Causal Paths, and Promising Research Directions. Psychological Bulletin, 132(3), 327–353. https://doi.org/10.1037/0033-2909.132.3.327.

Moen, P., Kelly, E. L., Fan, W., Lee, S.-R., Almeida, D., Kossek, E. E., & Buxton, O. M. (2016). Does a Flexibility/Support Organizational Initiative Improve High-Tech Employees’ Well-Being? Evidence from the Work, Family, and Health Network. American Sociological Review, 81(1), 134–164. https://doi.org/10.1177/0003122415622391.

Moen, P., Kelly, E. L., Tranby, E., & Huang, Q. (2011). Changing Work, Changing Health: Can Real Work-Time Flexibility Promote Health Behaviors and Well-Being? Journal of Health and Social Behavior, 52(4), 404–429. https://doi.org/10.1177/0022146511418979.

Muñoz, J. A., & Villanueva, I. (2022). Latino STEM Scholars, Barriers, and Mental Health: A Review of the Literature. Journal of Hispanic Higher Education, 21(1), 3–16. https://doi.org/10.1177/1538192719892148.

Munsch, C. L. (2016). Flexible Work, Flexible Penalties: The Effect of Gender, Childcare, and Type of Request on the Flexibility Bias. Social Forces, 94(4), 1567–1591. https://doi.org/10.1093/sf/sov122.

Nelson, L. K., Brewer, A., Mueller, A. S., O’Connor, D. M., Dayal, A., & Arora, V. M. (2023). Taking the Time: The Implications of Workplace Assessment for Organizational Gender Inequality. American Sociological Review, 88(4), 627–655. https://doi.org/10.1177/00031224231184264.

O’Connor, L. T., & Cech, E. A. (2018). Not Just a Mothers’ Problem: The Consequences of Perceived Workplace Flexibility Bias for All Workers. Sociological Perspectives, 61(5), 808–829. https://doi.org/10.1177/0731121418768235.

Parker, K., Horowitz, J. M., & Minkin, R. (2022, February 16). COVID-19 Pandemic Continues to Reshape Work in America. Pew Research Center. https://www.pewresearch.org/social-trends/2022/02/16/covid-19-pandemic-continues-to-reshape-work-in-america/.

Pascoe, E. A., & Smart Richman, L. (2009). Perceived Discrimination and Health: A Meta-Analytic Review. Psychological Bulletin, 135(4), 531–554. https://doi.org/10.1037/a0016059.

Perlow, L. A. (2012). Sleeping with Your Smartphone: How to Break the 24/7 Habit and Change the Way You Work. Harvard Business Press.

Pierce, J. L. (1996). Gender Trials: Emotional Lives in Contemporary Law Firms. University of California Press.

Ridgeway, C. L. (2011). Framed by Gender: How Gender Inequality Persists in the Modern World. Oxford University Press.

Ridgeway, C. L. (2014). Why Status Matters for Inequality. American Sociological Review, 79(1), 1–16. https://doi.org/10.1177/0003122413515997.

Rudman, L. A., & Mescher, K. (2013). Penalizing Men Who Request a Family Leave: Is Flexibility Stigma a Femininity Stigma? Journal of Social Issues, 69(2), 322–340. https://doi.org/10.1111/josi.12017.

Salvagioni, D. A. J., Melanda, F. N., Mesas, A. E., González, A. D., Gabani, F. L., & Andrade, S. M. de. (2017). Physical, Psychological and Occupational Consequences of Job Burnout: A Systematic Review of Prospective Studies. PLOS ONE, 12(10), e0185781. https://doi.org/10.1371/journal.pone.0185781.

Sanzari, C. M., Dennis, A., & Moss-Racusin, C. A. (2021). Should I Stay or Should I Go?: Penalties for Briefly De-Prioritizing Work or Childcare. Journal of Applied Social Psychology, 51(4), 334–349. https://doi.org/10.1111/jasp.12738.

Sassler, S. L., Smith, K. E., & Michelmore, K. (2023). Cohort Differences in Occupational Retention among Computer Science Degree Holders: Reassessing the Role of Family. Sociological Perspectives, 66(6), 1060–1083. https://doi.org/10.1177/07311214231195024.

Schieman, S., Glavin, P., & Milkie, M. A. (2009). When Work Interferes with Life: Work-Nonwork Interference and the Influence of Work-Related Demands and Resources. American Sociological Review, 74(6), 966–988. https://doi.org/10.1177/000312240907400606.

Schieman, S., & Young, M. (2010). The Demands of Creative Work: Implications for Stress in the Work–Family Interface. Social Science Research, 39(2), 246–259. https://doi.org/10.1016/j.ssresearch.2009.05.008.

Seron, C., Silbey, S. S., Cech, E., & Rubineau, B. (2016). Persistence Is Cultural: Professional Socialization and the Reproduction of Sex Segregation. Work and Occupations, 43(2), 178–214. https://doi.org/10.1177/0730888415618728.

Sharone, O. (2004). Engineering Overwork: Bell-Curve Management at a High-Tech Firm. In Fighting For Time: Shifting Boundaries of Work and Social Life (pp. 191–218). Russell Sage Foundation.

Stainback, K., & Tomaskovic-Devey, D. (2012). Documenting Desegregation: Racial and Gender Segregation in Private Sector Employment Since the Civil Rights Act. Russell Sage Foundation.

Steffan, B., & Loretto, W. (2024). Menopause, Work and Mid-Life: Challenging the Ideal Worker Stereotype. Gender, Work & Organization. https://doi.org/10.1111/gwao.13136.

Stier, H., & Yaish, M. (2014). Occupational Segregation and Gender Inequality in Job Quality: A Multi-Level Approach. Work, Employment and Society, 28(2), 225–246. https://doi.org/10.1177/0950017013510758.

Stokar, Y. N. (2024). Sense of Failure in End of Life Care: Perspectives from Physicians and Nurses. Social Science & Medicine, 348, 116805. https://doi.org/10.1016/j.socscimed.2024.116805.

Stone, P. (2007). Opting Out?: Why Women Really Quit Careers and Head Home. University of California Press.

Stone, P., & Lovejoy, M. (2021). Opting Back In: What Really Happens When Mothers Go Back to Work. University of California Press.

Taylor, C. J. (2016). “Relational by Nature”? Men and Women Do Not Differ in Physiological Response to Social Stressors Faced by Token Women. American Journal of Sociology, 122(1), 49–89. https://doi.org/10.1086/686698.

Thébaud, S., & Pedulla, D. S. (2022). When Do Work-Family Policies Work? Unpacking the Effects of Stigma and Financial Costs for Men and Women. Work and Occupations, 49(2), 229–263. https://doi.org/10.1177/07308884211069914.

Thébaud, S., & Taylor, C. J. (2021). The Specter of Motherhood: Culture and the Production of Gendered Career Aspirations in Science and Engineering. Gender & Society, 35(3), 395–421. https://doi.org/10.1177/08912432211006037.

Tian, T. Y., & Smith, E. B. (2024). Stretched Thin: How a Misalignment Between Allocation and Valuation Underlies the Paradox of Diversity Achievement in Higher Education. Administrative Science Quarterly, 00018392241247744. https://doi.org/10.1177/00018392241247744.

Tomaskovic-Devey, D., & Avent-Holt, D. (2019). Relational Inequalities: An Organizational Approach. Oxford University Press.

Trejo, J. (2020). The Burden of Service for Faculty of Color to Achieve Diversity and Inclusion: The Minority Tax. Molecular Biology of the Cell, 31(25), 2752–2754. https://doi.org/10.1091/mbc.E20-08-0567.

U.S. Department of Health and Human Services. (2024, August 28). U.S. Surgeon General Issues Advisory on the Mental Health and Well-Being of Parents [News Release]. https://www.hhs.gov/about/news/2024/08/28/us-surgeon-general-issues-advisory-mental-health-well-being-parents.html.

van de Brake, H. J., van der Vegt, G. S., & Essens, P. J. M. D. (2024). More Than Just a Number: Different Conceptualizations of Multiple Team Membership and Their Relationships with Emotional Exhaustion and Turnover. Journal of Applied Psychology, 109(5), 714–729. https://doi.org/10.1037/apl0001168.

Van Yperen, N. W., & Hagedoorn, M. (2003). Do High Job Demands Increase Intrinsic Motivation or Fatigue or Both? The Role of Job Control and Job Social Support. Academy of Management Journal, 46(3), 339–348. https://doi.org/10.5465/30040627.

von Känel, R., Princip, M., Holzgang, S. A., Fuchs, W. J., van Nuffel, M., Pazhenkottil, A. P., & Spiller, T. R. (2020). Relationship between Job Burnout and Somatic Diseases: A Network Analysis. Scientific Reports, 10(1), 18438. https://doi.org/10.1038/s41598-020-75611-7.

Walsh, J. (2013). Gender, the Work-Life Interface and Wellbeing: A Study of Hospital Doctors. Gender, Work & Organization, 20(4), 439–453. https://doi.org/10.1111/j.1468-0432.2012.00593.x.

Watts, J. H. (2009). ‘Allowed into a Man’s World’ Meanings of Work–Life Balance: Perspectives of Women Civil Engineers as ‘Minority’ Workers in Construction. Gender, Work & Organization, 16(1), 37–57. https://doi.org/10.1111/j.1468-0432.2007.00352.x.

Wharton, A. S. (2009). The Sociology of Emotional Labor. Annual Review of Sociology, 35, 147–165. https://doi.org/10.1146/annurev-soc-070308-115944.

Williams, J. (2001). Unbending Gender: Why Family and Work Conflict and What To Do About It. Oxford University Press.

Williams, J. C., Blair-Loy, M., & Berdahl, J. L. (2013). Cultural Schemas, Social Class, and the Flexibility Stigma. Journal of Social Issues, 69(2), 209–234. https://doi.org/10.1111/josi.12012.

Wingfield, A. H. (2010). Are Some Emotions Marked “Whites Only”? Racialized Feeling Rules in Professional Workplaces. Social Problems, 57(2), 251–268. https://doi.org/10.1525/sp.2010.57.2.251.

Wingfield, A. H. (2019). Flatlining: Race, Work, and Health Care in the New Economy. University of California Press.

Wingfield, A. H. (2021). The (Un)Managed Heart: Racial Contours of Emotion Work in Gendered Occupations. Annual Review of Sociology, 47(Volume 47, 2021), 197–212. https://doi.org/10.1146/annurev-soc-081320-114850.

Wingfield, A. H., & Chavez, K. (2020). Getting In, Getting Hired, Getting Sideways Looks: Organizational Hierarchy and Perceptions of Racial Discrimination. American Sociological Review, 85(1), 31–57. https://doi.org/10.1177/0003122419894335.

Wynn, A. T., & Correll, S. J. (2017). Gendered Perceptions of Cultural and Skill Alignment in Technology Companies. Social Sciences, 6(2), Article 2. https://doi.org/10.3390/socsci6020045.

Wynn, A. T., & Correll, S. J. (2018). Puncturing the Pipeline: Do Technology Companies Alienate Women in Recruiting Sessions? Social Studies of Science, 48(1), 149–164. https://doi.org/10.1177/0306312718756766.

Wynn, A. T., Fassiotto, M., Simard, C., Raymond, J. L., & Valantine, H. (2018). Pulled in Too Many Directions: The Causes and Consequences of Work-Work Conflict. Sociological Perspectives, 61(5), 830–849. https://doi.org/10.1177/0731121418774568.

Wynn, A. T., & Rao, A. H. (2020). Failures of Flexibility: How Perceived Control Motivates the Individualization of Work–Life Conflict. ILR Review, 73(1), 61–90. https://doi.org/10.1177/0019793919848426.

Yavaş, M. (2024). White-Collar Opt-Out: How “Good Jobs” Fail Elite Workers. American Sociological Review, 89(4), 761–788. https://doi.org/10.1177/00031224241263497.

Yavorsky, J. E., Keister, L. A., Qian, Y., & Thébaud, S. (2023). Separate Spheres: The Gender Division of Labor in the Financial Elite. Social Forces, 102(2), 609–632. https://doi.org/10.1093/sf/soad061.