Why Women Are More Burned Out Than Men

Statistics show that stress and burnout are affecting more women than men en masse. Why – and what happens next?

When Jia, a Manhattan-based consultant, read Sheryl Sandberg’s bestselling book Lean In in 2014, she resolved to follow the advice espoused by the chief operating officer of Facebook.

“I’d just graduated from an Ivy League business school, was super pumped up and loved the idea of leaning in,” says Jia, whose last name is being withheld to protect her professional reputation. “Learning to self-promote felt so empowering, and I was 100% ready to prove that I was the woman who could have it all: be a high-powered career woman and a great mother.”

But today, the 38-year-old strikes a different tone. For years, she says, she feels like she’s been overlooked for promotions and pay rises at work on account of her gender, particularly after becoming a mother in 2018. Since then, she’s picked up the brunt of childcare responsibilities because her husband, who is a banker, has tended to travel more frequently for work. That, she adds, has given her a misguided reputation among her colleagues and managers – the majority of whom are male – for not being professionally driven.

Then when Covid-19 hit, it was as if all the factors already holding her back were supercharged. When her daughter’s day care closed in March 2020, Jia became the default caregiver while trying to stay afloat at work. “I was extremely unmotivated because I felt like I was spending all hours of the day trying not to fall off an accelerating treadmill,” she explains. “But at the same time, I felt like I was being trusted less and less to be able to do a good job. I could feel my career slipping through my fingers and there was absolutely nothing I could do about it.”

In early 2021, Jia’s therapist told her she was suffering from burnout. Jia says she’d never struggled with her mental health before. “But now I’m just trying to get through each week while staying sane,” she says.

Jia’s story is symptomatic of a deeply ingrained imbalance in society that the pandemic has both highlighted and exacerbated. For multiple reasons, women, particularly mothers, are still more likely than men to manage a more complex set of responsibilities on a daily basis – an often-unpredictable combination of unpaid domestic chores and paid professional work.

I could feel my career slipping through my fingers and there was absolutely nothing I could do about it – Jia

Though the mental strain of mastering this balancing act has been apparent for decades, Covid-19 has cast a particularly harsh light on the problem. Statistics show that stress and burnout are affecting more women than men, and particularly more working mothers than working fathers. This could have multiple impacts for the post-pandemic world of work, making it important that both companies and wider society find ways to reduce this imbalance.

Unequal demands

Recent data looking specifically at burnout in women is concerning. According to a survey by LinkedIn of almost 5,000 Americans, 74% of women said they were very or somewhat stressed for work-related reasons, compared with just 61% of employed male respondents.

A separate analysis from workplace-culture consultancy a Great Place to Work and health-care start-up Maven found that mothers in paid employment are 23% more likely to experience burnout than fathers in paid employment. An estimated 2.35 million working mothers in the US have suffered from burnout since the start of the pandemic, specifically “due to unequal demands of home and work”, the analysis showed.

Women tend to be dealing with a more complex set of work and personal responsibilities, leading to stress (Credit: Getty)

Experts generally agree that there’s no single reason women burn out, but they widely acknowledge that the way societal structures and gender norms intersect plays a significant role. Workplace inequalities, for example, are inextricably linked to traditional gender roles.

In the US, women still earn an average of about 82 cents for each dollar earned by a man, and the gap across many countries in Europe is similar. Jia’s firm does not publish its gender pay-gap data, but she suspects that it’s significant. Moreover, she thinks many of her male peers earn more than her, something that causes her a huge amount of stress.

“The idea that I might be underselling myself is extremely frustrating, but I also don’t want to make myself unpopular by asking for more money when I’m already pushing the boundaries by asking my company to make accommodations for me having to care for my daughter,” she says. “It’s a constant internal battle.”

Research links lower incomes to higher stress levels and worse mental health in general. But several studies have also shown more specifically that incidences of burnout among women are greater because of differences in job conditions and the impact of gender on progression.

In 2018, researchers from University of Montreal published a study tracking 2,026 workers over the course of four years. The academics concluded that women were more vulnerable to burnout than men because women were less likely to be promoted than men, and therefore more likely to be in positions with less authority which can lead to increased stress and frustration. The researchers also found that women were more likely to head single-parent families, experience child-related strains, invest time in domestic tasks and have lower self-esteem – all things that can exacerbate burnout.

Nancy Beauregard, a professor at University of Montreal and one of the authors of that study, said that reflecting on her work back in 2018, it’s clear that Covid-19 has amplified the existing inequalities and imbalances that her team demonstrated through their research. “In terms of [the] sustainable development of the human capital of the workforce,” she says, “we’re not heading in a good direction.”

A pandemic catalyst

Brian Kropp, chief of human resources research at Gartner, a global research and advisory firm headquartered in Connecticut, US, agrees that while many of the factors fueling women’s burnout were in play before the pandemic, Covid-19 notably exacerbated some as it forced us to dramatically overhaul our living and working routines.

When the pandemic hit, many women found that their domestic responsibilities surged – making juggling work even harder (Credit: Getty)

Structures supporting parents’ and carers’ lives closed down, and in most cases, this excess burden fell on women. One study, conducted by academics from Harvard University, Harvard Business School and London Business School, evaluated survey responses from 30,000 individuals around the world and found that women – especially mothers – had spent significantly more time on childcare and chores during Covid-19 than they did pre-pandemic, and that this was directly linked to lower wellbeing. Many women had already set themselves up as the default caregiver within their households, and the pandemic obliterated the support systems that had previously allowed them to balance paid employment and domestic work.

That’s exactly what Sarah experienced in March 2020, when schools across New York first closed. “Initially the message was that schools would stay closed until the end of April, so that was my target: ‘Get to that point and you’ll be fine’,” recalls the Brooklyn-based 40-year-old. Now, more than 18 months into the pandemic, her two sons, aged 6 and 9, are only just reacquainting themselves with in-person learning, and Sarah’s life has changed dramatically.

In April 2020, for the first time ever, she started suffering from anxiety. The pressures of home-schooling her children while working as marketing executive for a large technology company overwhelmed her. She couldn’t sleep, worried constantly and felt depressed. Worst of all, she felt like whatever she did was inadequate because she didn’t have enough time to do anything well.

Six months into the pandemic, it was clear something had to change. Sarah’s husband, a lawyer, was earning much more than her, and had done so since they got married in 2008. So, in August 2020 the couple jointly decided that Sarah would leave her job to become a stay-at-home mother. “Before this, I never really knew what being burned out meant,” she says. “Now I know beyond a shadow of a doubt.”

Sarah’s experience is emblematic of a much broader trend. In September last year, just as the pandemic was gaining pace, more than 860,000 women dropped out of the US workforce, compared with just over 200,000 men. One estimate put the number of mothers who had quit the US workforce between February and September last year at 900,000, and the number of fathers at 300,000.

As women lost crucial social lifelines during lockdown which may have been emotional and physical outlets for stress, it’s clear that the abrupt avalanche of extra domestic responsibilities pushed many who were already busily juggling home and work life further than they could go.

‘What’s the cost?’

One of the greatest concerns workplace experts harbour is that poor mental health among women in the workplace could discourage future generations from setting ambitious professional goals, particularly if they want to start a family. That could exacerbate the gender inequalities that already exist in terms of pay and seniority in the labour market.

Data indicate that this is indeed a legitimate concern; statistics collected by CNBC and polling company SurveyMonkey earlier this year showed that the number of women describing themselves as “very ambitious” in terms of their careers declined significantly during the pandemic. Data from the US Census Bureau shows that over the first 12 weeks of the pandemic, the percentage of mothers between the ages of 25 and 44 not working due to Covid-19-related childcare issues grew by 4.8 percentage points, compared to no increase for men in the same age group.

In terms of [the] sustainable development of the human capital of the workforce, we’re not heading in a good direction – Nancy Beauregard

Equally, there are concerns about how new ways of working such as hybrid could impact on workplace gender equality. Research shows that women are more likely than men to work from home in a post-pandemic world, but there’s evidence that people who work from home are less likely to get promoted than those who have more face-time with managers. “Women are saying, I’m working just as hard and doing just as much, but because I’m working from home, I’m less likely to get promoted,” says Kropp. “That’s extremely demotivating.”

Dean Nicholson, head of adult therapy at London-based behavioural health clinic The Soke, suggests that perceptions of fairness – or otherwise – could impact on women’s workplace participation. “When the balance of justice is skewed against us in the workplace, then it’s invariably going to lead to negative feelings, not just towards the organisation, but in the way that we feel about ourselves and the value of our contribution, as well as where we’re positioned on a hierarchy of worth.”

To prevent an exodus of female talent, says Kropp, organisations must appreciate that old workplaces practices are no longer fit for purpose. Managers need to fundamentally rethink how companies must be structured in order to promote fairness and equality of opportunity, he says. That means pay equality and equal opportunities for promotion, as well as creating a culture of transparency where everyone – mothers, fathers and employees who are not parents – feels valued and can reach their professional potential while also accommodating what’s going on at home.

Steve Hatfield, global future of work leader for Deloitte, notes that mothers, especially those in senior leadership roles, are extremely important role models. “The ripple effect of what they’re seen to be experiencing right now has the potential to be truly profound on newer employees, and so it’s up to organisations to prove that they can accommodate and cater to the needs of all employees,” he says.

As such, Hephzi Pemberton, founder of the Equality Group, a London-based consultancy that focuses on inclusion and diversity in the finance and technology industry, emphasises the need for managers to be trained formally and to understand that the initiative to create a workplace that’s fit for purpose must come from the employer rather than the employee. “That’s absolutely critical to avoid the risk of burnout,” she says.

But Jia, who says she’s now on the brink of quitting her job, insists that notable changes need to happen in the home as well as the workplace. “What’s become abundantly clear to me through the pandemic is that we all have a role to play in understanding the imbalances that are created when stereotypical gender roles are blindly adhered to,” she says. “Yes, of course it sometimes makes sense for a woman to be the default caregiver or to take a step back from paid work, but we need to appreciate at what cost. This is 2021. Sometimes I wonder if we’re in the 1950s.”

By Josie Cox

Source: Why women are more burned out than men – BBC Worklife

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Related Contents:

 

Why Your Workforce Needs Data Literacy

Organizations that rely on data analysis to make decisions have a significant competitive advantage in overcoming challenges and planning for the future. And yet data access and the skills required to understand the data are, in many organizations, restricted to business intelligence teams and IT specialists.

As enterprises tap into the full potential of their data, leaders must work toward empowering employees to use data in their jobs and to increase performance—individually and as part of a team. This puts data at the heart of decision making across departments and roles and doesn’t restrict innovation to just one function. This strategic choice can foster a data culture—transcending individuals and teams while fundamentally changing an organization’s operations, mindset and identity around data.

Organizations can also instill a data culture by promoting data literacy—because in order for employees to participate in a data culture, they first need to speak the language of data. More than technical proficiency with software, data literacy encompasses the critical thinking skills required to interpret data and communicate its significance to others.

Many employees either don’t feel comfortable using data or aren’t completely prepared to use it. To best close this skills gap and encourage everyone to contribute to a data culture, organizations need executives who use and champion data, training and community programs that accommodate many learning needs and styles, benchmarks for measuring progress and support systems that encourage continuous personal development and growth.

Here’s how organizations can improve their data literacy:

1. LEAD

Employees take direction from leaders who signal their commitment to data literacy, from sharing data insights at meetings to participating in training alongside staff. “It becomes very inspiring when you can show your organization the data and insights that you found and what you did with that information,” said Jennifer Day, vice president of customer strategy and programs at Tableau.

“It takes that leadership at the top to make a commitment to data-driven decision making in order to really instill that across the entire organization.” To develop critical thinking around data, executives might ask questions about how data supported decisions, or they may demonstrate how they used data in their strategic actions. And publicizing success stories and use cases through internal communications draws focus to how different departments use data.

Self-Service Learning

This approach is “for the people who just need to solve a problem—get in and get out,” said Ravi Mistry, one of about three dozen Tableau Zen Masters, professionals selected by Tableau who are masters of the Tableau end-to-end analytics platform and now teach others how to use it.

Reference guides for digital processes and tutorials for specific tasks enable people to bridge minor gaps in knowledge, minimizing frustration and the need to interrupt someone else’s work to ask for help. In addition, forums moderated by data specialists can become indispensable roundups of solutions. Keeping it all on a single learning platform, or perhaps your company’s intranet, makes it easy for employees to look up what they need.

3.Measure

Success Indicators

Performance metrics are critical indicators of how well a data literacy initiative is working. Identify which metrics need to improve as data use increases and assess progress at regular intervals to know where to tweak your training program. Having the right learning targets will improve data literacy in areas that boost business performance.

And quantifying the business value generated by data literacy programs can encourage buy-in from executives. Ultimately, collecting metrics, use cases and testimonials can help the organization show a strong correlation between higher data literacy and better business outcomes.

4.Support

Knowledge Curators

Enlisting data specialists like analysts to showcase the benefits of using data helps make data more accessible to novices. Mistry, the Tableau Zen Master, referred to analysts who function in this capacity as “knowledge curators” guiding their peers on how to successfully use data in their roles. “The objective is to make sure everyone has a base level of analysis that they can do,” he said.

This is a shift from traditional business intelligence models in which analysts and IT professionals collect and analyze data for the entire company. Internal data experts can also offer office hours to help employees complete specific projects, troubleshoot problems and brainstorm different ways to look at data.

What’s most effective depends on the company and its workforce: The right data literacy program will implement training, software tools and digital processes that motivate employees to continuously learn and refine their skills, while encouraging data-driven thinking as a core practice.

For more information on how you can improve data literacy throughout your organization, read these resources from Tableau:

The Data Culture Playbook: Start Becoming A Data-Driven Organization

Forrester Consulting Study: Bridging The Great Data Literacy Gap

Data Literacy For All: A Free Self-Guided Course Covering Foundational Concepts

By: Natasha Stokes

Source: Why Your Workforce Needs Data Literacy

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Critics:

As data collection and data sharing become routine and data analysis and big data become common ideas in the news, business, government and society, it becomes more and more important for students, citizens, and readers to have some data literacy. The concept is associated with data science, which is concerned with data analysis, usually through automated means, and the interpretation and application of the results.

Data literacy is distinguished from statistical literacy since it involves understanding what data mean, including the ability to read graphs and charts as well as draw conclusions from data. Statistical literacy, on the other hand, refers to the “ability to read and interpret summary statistics in everyday media” such as graphs, tables, statements, surveys, and studies.

As guides for finding and using information, librarians lead workshops on data literacy for students and researchers, and also work on developing their own data literacy skills. A set of core competencies and contents that can be used as an adaptable common framework of reference in library instructional programs across institutions and disciplines has been proposed.

Resources created by librarians include MIT‘s Data Management and Publishing tutorial, the EDINA Research Data Management Training (MANTRA), the University of Edinburgh’s Data Library and the University of Minnesota libraries’ Data Management Course for Structural Engineers.

See also

How The Power Of Predictive Analytics Can Transform Business

Tableau analytics visual

With the acceleration of digital transformation in business, most CTOs, CIOs, and even middle management or analysts are now asking, “What’s next with data?” and what ongoing role will technology play in both digital and data transformations. Other questions that keep these individuals up at night include:

  • How can people throughout all organizational levels be more empowered to use data and help others make better decisions?
  • What prevents people from more deeply exploring and using data?
  • In what ways can analytics tools and methods help more people use data in the daily routine of business—asking questions, exploring hypotheses, and testing ideas?

With this in mind, plus observations and discussions with many Tableau customers and partners, it seems that today’s circumstances, behaviors, and needs make it the right time for predictive data analytics to help businesses and their people solve problems effectively.

Current realities and barriers to scale smarter decision-making with AI 

With growing, diverse data sets being collected, the analytics use cases to transform data into valuable insights are growing just as fast. Today, a wide range of tools and focused teams specialize in uncovering data insights to inform decision-making, but where organizations struggle is striking the right balance between activating highly technical data experts and business teams with deep domain experience.

Until now, using artificial intelligence (AI), machine learning (ML), and other statistical methods to solve business problems was mostly the domain of data scientists. Many organizations have small data science teams focused on specific, mission-critical, and highly scalable problems, but those teams usually have a long project list to handle.

At the same time though, there are a large number of business decisions that rely on experience, knowledge, and data—and that would greatly benefit from applying more advanced analysis techniques. People with domain knowledge and proximity to the business data could benefit greatly, if they had access to these techniques.

Instead, there’s currently a back-and-forth process of relying on data scientists and ML practitioners to build and deploy custom models—a cycle that lacks agility and the ability to iterate quickly. By the end, the data that the model was trained on could be stale and the process starts again. But organizations depend on business users to make key decisions daily that don’t rise to the priority level of their central data science team.

The opportunity to solve data science challenges

This is where there’s an opportunity to democratize data science capabilities, minimizing the trade-offs between extreme precision and control versus the time to insight—and the ability to take action on these insights. If we can give people tools or enhanced features to better apply predictive analytics techniques to business problems, data scientists can gain time back to focus on more complex problems. With this approach, business leaders can enable more teams to make data-driven decisions while continuing to keep up with the pace of business. Additional benefits gained from democratizing data science in this way include:

  • Reducing data exploration and prep work
  • Empowering analyst experts to deliver data science outputs at lower costs
  • Increasing the likelihood of producing successful models with more exploration of use cases by domain experts
  • Extending, automating, and accelerating analysis for business groups and domain experts
  • Reducing time and costs spent on deploying and integrating models
  • Promoting responsible use of data and AI with improved transparency and receiving guidance on how to minimize or address bias

Business scenarios that benefit from predictive analytics 

There are several business scenarios where predictive capabilities can be immensely useful.

Sales and marketing departments can apply it to lead scoring, opportunity scoring, predicting time to close, and many other CRM-related cases. Manufacturers and retailers can use it to help with supply chain distribution and optimization, forecasting consumer demand, and exploring adding new products to their mix. Human resources can use it to assess the likelihood of candidates accepting an offer, and how they can adjust salary and benefits to meet a candidate’s values. And companies can use it to explore office space options and costs. These are just a few of the potential scenarios.

A solution to consider: Tableau Business Science

We are only at the beginning of exploring what predictive capabilities in the hands of people closely aligned with the business will unlock. AI and ML will continue to advance. More organizations, in a similar focus as Tableau, will also keep looking for techniques that can help people closest to the business see, understand, and use data in new ways to ask and answer questions, uncover insights, solve problems, and take action.

This spring Tableau introduced a new class of AI-powered analytics that gives predictive capabilities to people who are close to the business. In this next stage of expanded data exploration and use, we hope business leaders embrace data to help others make better decisions, and to provide transparent insight into the factors influencing those decisions.

When people can think with their data—when analysis is more about asking and answering questions than learning complex software or skills—that’s when human potential will be unleashed, leading to amazing outcomes. Learn more about Tableau Business Science, what this technology gives business teams, and the value it delivers to existing workflows.

Olivia Nix is a Senior Manager of Product Marketing at Tableau. She leads a team focused on the use of AI and ML in analytics and engagement, including how to use technology to enable more people in organizations to make data-driven decisions. Olivia has been at Tableau for four years where she has worked closely with development teams on new product launches. Prior to Tableau, Olivia worked as an analyst at the Pew Center on Global Climate Change (now C2ES) and Johnson Controls. She has her MBA from the UCLA Anderson School of Management.

Source: How The Power Of Predictive Analytics Can Transform Business

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Critics:

Predictive analytics encompasses a variety of statistical techniques from data mining, predictive modelling, and machine learning that analyze current and historical facts to make predictions about future or otherwise unknown events.

In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision-making for candidate transactions.

The defining functional effect of these technical approaches is that predictive analytics provides a predictive score (probability) for each individual (customer, employee, healthcare patient, product SKU, vehicle, component, machine, or other organizational unit) in order to determine, inform, or influence organizational processes that pertain across large numbers of individuals, such as in marketing, credit risk assessment, fraud detection, manufacturing, healthcare, and government operations including law enforcement.

Predictive analytics is used in actuarial science,marketing,business management, sports/fantasy sports, insurance,policing, telecommunications,retail, travel, mobility, healthcare, child protection, pharmaceuticals,capacity planning, social networking and other fields.

One of the best-known applications is credit scoring,[1] which is used throughout business management. Scoring models process a customer’s credit history, loan application, customer data, etc., in order to rank-order individuals by their likelihood of making future credit payments on time.

Predictive analytics is an area of statistics that deals with extracting information from data and using it to predict trends and behavior patterns. The enhancement of predictive web analytics calculates statistical probabilities of future events online. Predictive analytics statistical techniques include data modeling, machine learning, AI, deep learning algorithms and data mining.Often the unknown event of interest is in the future, but predictive analytics can be applied to any type of unknown whether it be in the past, present or future.

For example, identifying suspects after a crime has been committed, or credit card fraud as it occurs.The core of predictive analytics relies on capturing relationships between explanatory variables and the predicted variables from past occurrences, and exploiting them to predict the unknown outcome. It is important to note, however, that the accuracy and usability of results will depend greatly on the level of data analysis and the quality of assumptions.

Predictive analytics is often defined as predicting at a more detailed level of granularity, i.e., generating predictive scores (probabilities) for each individual organizational element. This distinguishes it from forecasting. For example, “Predictive analytics—Technology that learns from experience (data) to predict the future behavior of individuals in order to drive better decisions.”In future industrial systems, the value of predictive analytics will be to predict and prevent potential issues to achieve near-zero break-down and further be integrated into prescriptive analytics for decision optimization.

See also

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