In 2022, most Polish employees stated that employers should offer flexible working hours to improve the work-life balance. Only ** percent thought that support for their health care would improve their work-life balance.

In a survey conducted in New Zealand in 2022, less than half of the respondents reported that their work-life balance was excellent or good. Around ** percent of participants considered their work-life balance to be poor or very poor.

Being able to work from home before or after a business trip was the most common work-life balance measure offered to corporate travelers by travel managers worldwide as of **********, according to a global survey. At that time, over ** percent of travel managers surveyed reported that their companies had such policy in place.

In 2022, around ** percent of employees working remotely worldwide reported that their work-life boundaries were somewhat healthy. In contrast, **** percent of employees reported having somewhat unhealthy work-life boundaries that year. Generally, the vast majority of remote workers globally stated that they had very or somewhat healthy work-life boundaries.

In a survey conducted in New Zealand in 2022, over ** percent of respondents who reported having a good work-life balance indicated that they have flexible working hours. The same share of respondents reported being able to switch off from work when they leave.

Allowing bleisure trips – the mix of leisure and business travel – was cited as the favorite work-life balance support measure of business travelers surveyed worldwide in early 2022. Meanwhile, at 53 percent, extra time off to compensate for the work trips was chosen as the second most desired work-life balance measure that could be offered.

The conditions under which Canadians work, including work hours, health and safety, wages and benefits, represent an important component of quality of life and well-being. The results of the Survey of Employees under Federal Jurisdiction (SEFJ) offer insights into all these aspects for employees working in federally regulated workplaces, including information on differences by gender. The SEFJ gathers information on working conditions, including compensation and leave, working time, health and safety, harassment and violence, discrimination, collective bargaining, and work-life balance. This data was collected in 2022 and is provided by the Strategic Policy, Analysis and Workplace Information Directorate at the Labour Program.

There is more to life than the cold numbers of GDP and economic statistics. This dataset contains the 2018 data of the Better Life Index which allows you to compare well-being across countries as well as measuring well-being, based on 11 topics the OECD has identified as essential, in the areas of material living conditions and quality of life. Abstract: Your Better Life Index aims to involve citizens in the debate on measuring the well-being of societies, and to empower them to become more informed and engaged in the policy-making process that shapes all our lives. Each of the 11 topics of the Index is currently based on one to three indicators. Within each topic, the indicators are averaged with equal weights. The indicators have been chosen on the basis of a number of statistical criteria such as relevance (face-validity, depth, policy relevance) and data quality (predictive validity, coverage, timeliness, cross-country comparability etc.) and in consultation with OECD member countries. These indicators are good measures of the concepts of well-being, in particular in the context of a country comparative exercise. Other indicators will gradually be added to each topic. Notes: Data cannot be compared between different editions of the Better Life Index. For more information on change over time, please contact wellbeing@oecd.org.

In a survey conducted in New Zealand in 2022, around 60 percent of respondents who reported having a poor work-life balance indicated that their workload is high. Almost half of the respondents reported financial pressures as a key factor affecting their work-life balance satisfaction.

TitleInterview-Based Stress Assessment Dataset

OverviewThe dataset includes stress evaluations (6 grades) assessed by interviews of 50 Japanese workers (49 completed follow-up), as well as self-reported stress and attribute information and personality information measured at the pre and one-month follow-up.

Data SourceInterviews were conducted between December 2022 and January 2023. The average follow-up period was 34.2 days.The main variables were interview-based stress evaluation, with self-reported stress (stress load, mental symptoms and physical symptoms from the Brief Job Stress Questionnaire), well-being (life satisfaction and happiness), and burnout were measured pre and 1 month later. Interview-based stress evaluations were conducted by two occupational health professionals in addition to an evaluation by the interviewer, a psychologist.

Data Description## main variables are total (time 1 self-reported stress), burnout, wellbeing, meanStressEv (mean overall stress ratings of interviewer and two evaluators), T2_loadAll, T2_mental, T2_physical, T2_burnout, and T2_wellbeing

no: Record number or identifier.age: Age of the individual in years.gender: Gender of the individual. Possible values include 'male', 'female', etc.height_cm: Height of the individual in centimeters.weight_kg: Weight of the individual in kilograms.BMI: Body Mass Index, calculated based on height and weight.drinking_freq: Frequency of alcohol consumption. Example values might be 'daily', 'weekly', 'monthly', etc.smoking_habits: Smoking habits of the individual. Possible values include 'smoker', 'non-smoker', etc.money_spending_hobby: Attitude towards spending money on hobbies. Describes how much an individual spends on their hobbies.employment_status: Current employment status. Possible values include 'employed', 'unemployed', 'self-employed', etc.full_time: employment_statuspart_time: employment_statusdiscretionary: employment_statusside_job: This variable likely indicates whether the individual has a side job in addition to their primary employment. The values could be binary (yes/no) or provide more detail about the nature of the side job.work_type: This variable probably categorizes the type of work the individual is engaged in. It could include categories such as 'full-time', 'part-time', 'contract', 'freelance', etc.fixedHours: This variable might indicate whether the individual's work schedule has fixed hours. It could be a binary variable (yes/no) indicating the presence or absence of a fixed work schedule.rotationalShifts: This variable likely denotes whether the individual works in rotational shifts. It could be a binary (yes/no) variable or provide details on the shift rotation pattern.flexibleShifts: This variable possibly reflects if the individual has flexible shift options in their work. This could involve varying start and end times or the ability to switch shifts.flexTime: This variable might indicate the presence of 'flextime' in the individual's work arrangement, allowing them to choose their working hours within certain limits.adjustableWorkHours: This variable probably denotes whether the individual has the ability to adjust their work hours, suggesting a degree of flexibility in their work schedule.discretionaryWork: This variable could indicate whether the individual's work involves a degree of discretion or autonomy in decision-making or task execution.nightShift: This variable likely indicates if the individual works night shifts. It could be a simple binary (yes/no) or provide details about the frequency or regularity of night shifts.remote_work_freq: This variable probably measures the frequency of remote work. It could include categories like 'never', 'sometimes', 'often', or 'always'.primary_job_industry: This variable likely categorizes the industry sector of the individual's primary job. It could include sectors like 'technology', 'healthcare', 'education', 'finance', etc.ind: industryind.manu–ind.gove: binary coding of industryprimary_job_role: This variable likely represents the specific role or position held by the individual in their primary job. It could include titles like 'manager', 'engineer', 'teacher', etc.job: jobjob.admi–job.carClPa: binary coding of jobjob_duration_years: This variable probably indicates the duration of the individual's current job in years. It typically measures the length of time an individual has been in their current job role.years: Without additional context, this variable could represent various time-related aspects, such as years of experience in a particular field, age in years, or years in a specific role. It generally signifies a duration or period in years.months: Similar to 'years', this variable could refer to a duration in months. It might represent age in months (for younger individuals), months of experience, or months spent in a current role or activity.job_duration_months: This variable is likely to indicate the total duration of the individual's current job in months. It's a more precise measure compared to 'job_duration_years', especially for shorter employment periods.working_days_per_week: This variable probably denotes the number of days the individual works in a typical week. It helps to understand the work pattern, whether it's a standard five-day workweek or otherwise.work_hours_per_day: This variable likely measures the average number of hours the individual works each day. It can be used to assess work-life balance and overall workload.job_workload: This variable might represent the overall workload associated with the individual's job. This could be subjective (based on the individual's perception) or objective (based on quantifiable measures like hours worked or tasks completed).job_qualitative_load: This variable likely assesses the qualitative aspects of the job's workload, such as the level of mental or emotional stress, complexity of tasks, or level of responsibility.job_control: This variable probably measures the degree of control or autonomy the individual has in their job. It could assess how much freedom they have in making decisions, planning their work, or the flexibility in how they perform their duties.hirou_1–hirou_7: Working Conditions of Fatigue Accumulation Checklisthirou_kinmu: Sum of Working Conditions of Fatigue Accumulation ChecklistWH_1–WH_2: Items related to workaholicworkaholic: Sum of items related to workaholicWE_1–WE_3: Items related to work engagementengagement: Sum of items related to work engagementrelationship_stress: This variable likely measures stress stemming from personal relationships, possibly including family, romantic partners, or friends.future_uncertainty_stress: This variable probably captures stress related to uncertainties about the future, such as career prospects, financial stability, or life goals.discrimination_stress: This variable indicates stress experienced due to discrimination, possibly based on factors like race, gender, age, or other personal characteristics.financial_stress: This variable measures stress related to financial matters, such as income, expenses, debt, or overall financial security.health_stress: This variable likely assesses stress concerning personal health or the health of loved ones.commuting_stress: This variable measures stress associated with daily commuting, such as traffic, travel time, or transportation issues.irregular_lifestyle: This variable probably indicates the presence of an irregular lifestyle, potentially including erratic sleep patterns, eating habits, or work schedules.living_env_stress: This variable likely measures stress related to the living environment, which could include housing conditions, neighborhood safety, or noise levels.unrewarded_efforts: This variable probably assesses feelings of stress or dissatisfaction due to efforts that are perceived as unrewarded or unacknowledged.other_stressors: This variable might capture additional stress factors not covered by other specific variables.coping: This variable likely assesses the individual's coping mechanisms or strategies in response to stress.support: This variable measures the level of support the individual perceives or receives, possibly from friends, family, or professional services.weekday_bedtime: This variable likely indicates the typical bedtime of the individual on weekdays.weekday_wakeup: This variable represents the typical time the individual wakes up on weekdays.holiday_bedtime: This variable indicates the typical bedtime of the individual on holidays or non-workdays.holiday_wakeup: This variable measures the typical wake-up time of the individual on holidays or non-workdays.avg_sleep_duration: This variable likely represents the average duration of sleep the individual gets, possibly averaged over a certain period.weekday_bedtime_posix: This variable might represent the weekday bedtime in POSIX time format.weekday_wakeup_posix: Similar to bedtime, this represents the weekday wakeup time in POSIX time format.holiday_bedtime_posix: This variable likely indicates the holiday bedtime in POSIX time format.holiday_wakeup_posix: This represents the holiday wakeup time in POSIX time format.weekday_bedtime_posix_hms: This variable could be the weekday bedtime in POSIX time format, specifically in hours, minutes, and seconds.weekday_wakeup_posix_hms: This variable might represent the weekday wakeup time in POSIX time format in hours, minutes, and seconds.holiday_bedtime_posix_hms: The holiday bedtime in POSIX time format, detailed to hours, minutes, and seconds.holiday_wakeup_posix_hms: The holiday wakeup time in POSIX time format, in hours, minutes, and seconds.weekday_sleep_duration: This variable likely measures the duration of sleep on weekdays.holiday_sleep_duration: This variable measures the duration of sleep on holidays or non-workdays.delta_sleep_h_w: This variable might represent the difference in sleep duration between holidays and

In 2022, the most important aspect of a workplace for both GenZ and millennials was to have a good work-life balance. Similarly, almost 30 percent of both GenZ and millenials also chose their workplace due to learning and development opportunities.

The employee engagement survey is distributed to all classified and exempt employees in the Executive Branch annually. Survey participation is voluntary, and the scores only reflect those who participated in the process. An “Employee Engagement Score” is calculated as an index of overall employee engagement. The index is the average of seven components of employee engagement, each based on a subset of questions in the employee engagement survey: Growth – personal growth and development, Balance – work-life, Supervisor – support, recognition and feedback, Communication – value employee voices, ideas, opinions, Peers – positive relationships in the workplace, Alignment – understanding the link between one’s job and the organization’s mission, Satisfaction – work and employer. The score is the average of the seven components of engagement listed above. For 2022 the average employee engagement score was 3.84 out of a possible 5.0. Scores increased 3.6% from 2014 to 2017. There was a slight drop from 2017 to 2021. The 2022 survey shows overall engagement score going up slightly to be just shy of the highest score (2017; 3.85) we've seen since the survey began. Please see the online resource for more detailed information on the employee engagement survey and how the engagement score was calculated. https://humanresources.vermont.gov/document/employee-engagement-survey-results-2022

Here are a few use cases for this project:

1. Workplace Efficiency Evaluation: Enterprises can use the "Office_productivity" model to analyze and optimize employee productivity. The system can identify whether workspaces are being used efficiently or are unoccupied (Not_working), hence making resource reallocation decisions easier.

2. Smart Building Management: Commercial property managers can use the data to better manage building resources like lighting, heating, and air conditioning. If no activity (Not_working) is identified, these resources can be adjusted or turned off to save energy.

3. Employee Wellness Monitoring: By identifying periods of intensive work (Working) versus idle time (Not_working), the model can be used to promote balanced work-rest cycles, reminding employees to take necessary breaks for their wellness and productivity.

4. Meeting Room Scheduling: Organisations can optimize meeting room usage by tracking periods of rooms being occupied (Working) versus empty (Not_working). This insight can improve the reservation and scheduling system, freeing up space for necessary meetings.

5. Remote Working Evaluation: The model can be used to track productivity in a remote working setting by identifying active work hours (Working) and inactive periods (Not_working). The data can be used to make adjustments for improved work-life balance and productivity.

A set of interviews with NHS COVID-19 frontline staff to investigate the influence of COVID deployment on non-technical factors for healthcare delivery (leadership, social support & cohesion, communication, shared mental models, co-ordination) and expected moderating factors (occupational background, preparedness, work-life balance and home situation, proximity, workforce allocation models) and the impact on perceived teamwork, performance, individual team member well-being, resilience and team member employment retention intentions for NHS COVID-team members. The interviews with medical staff consisted of demographic questions collecting some special category data (e.g., ethnicity, job title, living arrangements during COVID), a 12-item standardised measure of wellbeing (administered using the GHQ-12, a short form General Health Questionnaire) and an 8 item Work Life Balance Scale (Schwartz et al., 2019; Sexton et al., 2017). These are not included in the interview transcripts. The interview schedule then followed a topic based semi-structured component (informed by themes identified in our previous work (Reid et al., 2018; 2016; Schilling, 2019), the wider literature, and our preliminary conceptual framework across these four main areas: 1) the creation of teams and the experience of teamwork, social support, shared communication patterns, co- ordination and mental models; 2) the role of leaders in establishing teamwork, social support, shared communication patterns, co-ordination and mental models; 3) perceived individual and team performance, well-being, resilience and retention intentions; 4) moderating factors including occupational background, preparedness, home life, work-life balance and any other issues arising during COVID-team membership.

The BYOD Enterprise Mobility Market size was valued at USD 84.22 Billion in 2024 and is projected to reach USD 248.73 Billion by 2031, growing at a CAGR of 15.99% from 2024 to 2031.

Global BYOD Enterprise Mobility Market Drivers

The market drivers for the BYOD Enterprise Mobility Market can be influenced by various factors. These may include:

Growing Mobile Workforce: Using personal devices for work is becoming more and more necessary as remote and mobile work become more common. Allowing workers to use their own devices can increase flexibility and productivity (BYOD).

Cost Savings: By reducing the need for businesses to spend money on employee device purchases, BYOD can save costs for businesses. Employees use their own devices instead, which lowers the organization's expenses for hardware and upkeep.

Worker Satisfaction and Productivity: Providing familiar devices to employees can enhance their job satisfaction and productivity. Since they are more accustomed to and skilled in using their own devices, employees frequently opt to use them.

Technological Advancements: The capacity of personal devices for work-related tasks is improved by the ongoing development of mobile technology, such as smartphones and tablets. This promotes the adoption of BYOD policies by more companies.

Flexibility and Convenience: Bring your own device (BYOD) policy gives staff members the freedom to work whenever and wherever they choose. Employee convenience and work-life balance may both benefit from this flexibility.

BYOD Security Solutions: As BYOD becomes more widely used, there is a rising need for security solutions designed specifically to handle the special difficulties posed by personal devices accessing company data. This covers mobile application management (MAM), mobile device management (MDM), and other security protocols to safeguard private company data.

Global Connectivity: Regardless of location, users may effortlessly access corporate resources from personal devices thanks to the widespread use of wireless networks and high-speed internet connectivity.

Compliance and Regulations: To ensure compliance with legal requirements, organizations must develop safe BYOD policies and solutions. These are driven by compliance requirements and regulations surrounding data protection and privacy, such as the CCPA in California and the GDPR in Europe.

Business Continuity and Remote Work: The COVID-19 pandemic has sped up the adoption of remote work procedures, and for many organizations, BYOD is a crucial part of their business continuity strategy. It is anticipated that remote work will continue to be common as the pandemic fades, which will keep BYOD solutions in demand.

Integration with Cloud Services: Bringing BYOD into line with cloud-based services and apps facilitates easy access to company data and resources from personal devices, which encourages more businesses to embrace BYOD.

According to Cognitive Market Research, the global Gig Economy market size will be USD 561245.2 million in 2024. It will expand at a compound annual growth rate (CAGR) of 17.20% from 2024 to 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD 224498.08 million in 2024 and will grow at a compound annual growth rate (CAGR) of 15.4% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 168373.56 million in 2024 and will grow at a compound annual growth rate (CAGR) of 15.7% from 2024 to 2031.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 129086.40 million in 2024 and will grow at a compound annual growth rate (CAGR) of 19.2% from 2024 to 2031.
Latin America had a market share of more than 5% of the global revenue with a market size of USD 28062.26 million in 2024 and will grow at a compound annual growth rate (CAGR) of 16.6% from 2024 to 2031.
Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD 11224.90 million in 2024 and will grow at a compound annual growth rate (CAGR) of 16.9% from 2024 to 2031.
The transportation-based services category is the fastest growing segment of the Gig Economy industry

Market Dynamics of Gig Economy Market

Key Drivers for Gig Economy Market

Changing work approach driving the gig economy

The shift in work approach, particularly among younger generations, is a key driver of the gig economy. Millennials and Gen Z are prioritizing work that aligns with their passions and interests, seeking flexibility and autonomy over traditional career paths. The shift is majorly driven by the desire for work-life balance, alternate income sources and ability to work remotely, from anywhere. This shift has been on the rise particularly since the global pandemic that had pushed people to work from their homes and across various digital platforms. Businesses are embracing the flexible work arrangements to reduce costs and access specialized skills.

For instance,

Global research from the World Employment Confederation (WEC) finds that 83% of senior executives say that, since the pandemic, workers place as much value on flexibility in terms of when and where they work as on compensation.
A 2022 LinkedIn survey found that Gen Z workers were the cohort most likely to have left a role because of a perceived lack of flexibility (72% fell into this category, compared with 69% of Millennials, 53% of Gen X and 59% of Baby Boomers).
53% of Gen Z workers who freelance are moving away from traditional 9-to-5 jobs in favor of full-time freelancing.

(Source:https://insights.wecglobal.org/the-work-we-want/home/workplace-policy-younger-generations#:~:text=For%20example%2C%20new%20global%20research,the%20priorities%20of%20younger%20people. )

(Source: https://www.upwork.com/resources/gig-economy-statistics )

The digitalization of work is fueling demand for more gigs

Driven by technological advances and the increasing digitalization of skills and processes, the gig economy has expanded rapidly, by making work accessible to more people around the globe. The rise of online marketplaces like Upwork, Uber and Fiverr have made it easier for freelancers to find work and for companies to access a more flexible workforce. Improved technology and digital infrastructure have further made it easier and cheaper to connect with gig workers. The rise of e-commerce platforms and on-demand services such as ride-sharing, food delivery rely majorly on gig workers, contributing significantly to the growth of gig economy. Digital tools like instant messaging and video conferencing along with collaborative platforms like slack, MS Teams make it easy for employees to communicate from anywhere at any time.

With Artificial intelligence (AI) becoming one of the fastest-growing sectors and skill sets for independent professionals, AI has contributed to the growth of gig economy. AI is significantly impacting the gig economy by automating tasks, improving matching of workers and jobs. AI powered platforms also help streamline the recruitment process for businesses, by matching candidates with suitable projects based on skills, experience and availability.

For instance,

95% of respondents said generative AI makes them more competitive an...

A survey of employees in the United States in 2022 found that being overworked was the most common cause of work-related stress followed by a lack of work-life balance. This statistic shows the percentage of employees in the United States who stated the following caused them the most work-related stress.

In 2022, the International Social Survey Programme (ISSP) focused on family, work and gender roles. Questions covered issues such as family leave, domestic work and perceptions of gender differences in both employment and childcare. This data consists of a survey of Finns. Respondents were first asked to rate statements on work-life balance and the roles of women and men in the family and in working life. They were also asked how children affect the freedom, finances, employment, career opportunities and social status of parents. Next, respondents' views on family leave were examined. Questions included how leave should be shared between parents and how long paid family leave should be. They also asked about who should cover the costs of family leave, childcare and services for the elderly. In terms of time use, the survey asked how many hours per week the respondent and his/her spouse spend on housework and looking after family members. The survey also asked who takes care of the family finances and who in the family takes care of everyday chores such as planning activities or cooking. Respondents were also asked to reflect on the importance of family ties and friendship in life, as well as on the division of child-rearing responsibilities between the genders. Background variables included, among others, respondent's gender, year of birth, marital status, level of education, occupation, voting behaviour, gross income, current employment status, household size and metropolitan area. In addition, background variables include the education, occupation and labour market status of the respondent's spouse.

Rachmad, Yoesoep Edhie. 2022. Stress Management Theory. Saarbrücker Saar Buch Internationaler Verlag, Spezialausgabe 2022. https://doi.org/10.17605/osf.io/5ut3k

Stress Management Theory, introduced by Yoesoep Edhie Rachmad in 2022, emerged from the need to understand and address the increasing stress in modern life. In a world filled with pressure and rapid change, stress has become a major issue affecting individual well-being and organizational performance. This theory was developed to provide guidance on effectively managing stress through a holistic approach that encompasses physical, emotional, and environmental aspects. Stress Management Theory defines stress management as a set of strategies and techniques used to reduce, manage, and control stress levels to prevent negative impacts on health and performance. The basic concept of this theory is that stress is the body's natural response to pressure, but if managed well, it can serve as a positive motivator. The theory emphasizes the importance of recognizing stress triggers, understanding the body's response to stress, and developing skills to manage stress effectively. This theory is based on the phenomenon that many individuals and organizations often feel overwhelmed by excessive stress, leading to various physical and mental health problems, as well as decreased productivity and job satisfaction. For example, high work pressure, complex life demands, and lack of time for relaxation can result in dangerous stress levels. This phenomenon indicates the need for a systematic approach to identify, reduce, and manage stress to achieve optimal well-being and performance. The working principles of Stress Management Theory involve several key steps. First, it is important to recognize and understand the unique sources of stress for each individual. Second, develop appropriate strategies to reduce or manage stress, such as relaxation techniques, exercise, and time management. Third, consistently apply these techniques and adjust them according to changing conditions and individual needs. The theory also highlights the importance of social support and a supportive environment in managing stress. Key indicators of this theory include perceived stress levels, frequency and intensity of stress responses, and the effectiveness of stress management strategies used. Other indicators include sleep quality, physical and mental health, and life and job satisfaction. These indicators can be measured through surveys, interviews, and observations in various contexts. They help evaluate the extent to which individuals and organizations succeed in managing stress and achieving well-being. Operational variables of Stress Management Theory include measuring stress levels, effectiveness of stress management techniques, and quality of social support. These variables can be measured through surveys, self-assessments, and health data analysis. This data provides insights into how individuals and organizations manage stress and how interventions can be designed to improve well-being and performance. This theory can be applied in various fields such as Human Resource Management (HRM), Psychology, Education, Social Communication, and Health. In HRM, the theory helps develop programs that enhance employee well-being and reduce work stress levels. In Psychology, the theory is useful for understanding how individuals respond to stress and developing effective interventions. In Education, the theory supports the development of curricula that promote the well-being of students and staff. In Social Communication, the theory guides strategies to improve community engagement and well-being. In Health, the theory helps design programs that support patients and healthcare professionals in managing stress. The success of applying this theory is determined by several key factors. Support from leaders and an organizational culture that supports stress management are crucial. Additionally, adequate training and resources to develop stress management skills are key factors. Success is also influenced by the ability to leverage social support and create an environment that supports individual engagement and well-being. Implementing this theory requires a structured and ongoing approach. The first step is to identify needs and barriers in stress management and develop appropriate programs. Effective strategies include providing training on stress management techniques, developing policies that support mental well-being, and creating mechanisms for feedback and evaluation. Continuous evaluation and strategy adjustments based on individual feedback are essential for ensuring long-term success. Challenges in applying this theory include resistance to change, lack of understanding of the importance of stress management, and limited resources for developing and implementing stress management programs. However, with strong support from leaders and a supportive organizational culture, as well as ongoing education, these challenges can be overcome. Success also depends on the ability to create an environment that supports individual engagement and stress management, and the ability to manage conflicts and obstacles that may arise in the stress management process. Stress Management Theory emphasizes the importance of managing stress to achieve optimal well-being and performance. This theory provides a comprehensive framework for understanding and facilitating stress management across various fields. With proper application, this theory can help individuals and organizations enhance their ability to manage stress, better face challenges, and create a positive impact on performance and well-being.   Table of Contents Stress Management Theory By Yoesoep Edhie Rachmad Published by Saarbrücker Saar Buch Internationaler Verlag, Spezialausgabe 2022 DOI: https://doi.org/10.17605/osf.io/5ut3k

Chapter 1: Introduction to Stress Management Theory 1.1 Understanding Stress and Its Impact............. 1 1.2 Historical Background of Stress Research............. 12 1.3 Stress in Modern Life: A Growing Challenge............. 26 Chapter 2: Theoretical Foundations of Stress Management 2.1 Defining Stress Management and Key Concepts............. 38 2.2 Physiological and Psychological Responses to Stress............. 51 2.3 Stress as a Positive Force: Motivational Aspects............. 64 Chapter 3: Identifying Stress Triggers 3.1 Workplace Stress: Causes and Solutions............. 78 3.2 Personal Life Stressors: Balancing Demands............. 91 3.3 Recognizing Early Signs of Stress............. 104 Chapter 4: Techniques for Effective Stress Management 4.1 Relaxation Techniques: Meditation and Mindfulness............. 118 4.2 Physical Exercise and Stress Reduction............. 132 4.3 Cognitive Behavioral Approaches to Stress............. 145 Chapter 5: The Role of Social Support in Managing Stress 5.1 The Importance of a Supportive Environment............. 160 5.2 Building Strong Social Networks............. 174 5.3 Leveraging Support Systems in the Workplace............. 188 Chapter 6: Stress Management in Organizational Settings 6.1 Developing Stress-Reduction Programs for Employees............. 202 6.2 Promoting a Healthy Work-Life Balance............. 216 6.3 Leadership’s Role in Reducing Work Stress............. 230 Chapter 7: Psychological Strategies for Managing Stress 7.1 Emotional Regulation Techniques............. 246 7.2 Managing Anxiety and Building Resilience............. 260 7.3 Self-Reflection and Stress Awareness............. 274 Chapter 8: Educational Approaches to Stress Management 8.1 Stress Management for Students and Educators............. 288 8.2 Integrating Stress Awareness into School Curricula............. 303 8.3 Strategies for Reducing Academic Stress............. 317 Chapter 9: Health Applications of Stress Management Theory 9.1 Stress and Its Impact on Physical Health............. 332 9.2 Managing Stress in Healthcare Professionals............. 345 9.3 Designing Health Programs to Address Patient Stress............. 360 Chapter 10: Monitoring and Evaluating Stress Management Effectiveness 10.1 Measuring Stress Levels: Tools and Techniques............. 374 10.2 Assessing the Effectiveness of Stress Management Programs............. 388 10.3 Continuous Improvement in Stress Management Strategies............. 402 Chapter 11: Case Studies in Stress Management 11.1 Stress Management in High-Pressure Environments............. 418 11.2 Organizational Success through Stress Reduction Initiatives............. 433 11.3 Real-World Examples of Effective Stress Management............. 447

Appendices • Appendix A: Glossary of Stress Management Terms............. 463 • Appendix B: Sample Stress Management Program Outline............. 478 • Appendix C: Stress Self-Assessment Tools............. 492

References .................................................... 510 Index .............................................................. 532 Acknowledgments ........................................ 556   AUTHOR PROFILE In 2016, the author earned the title of Doctor of Humanity, hold a Ph.D. in Information Technology and a DBA in General Management. Since 2016, the author has been teaching at international universities in Malaysia, Singapore, Thailand, and the USA. In 1999, the author founded the Education Training Centre (ETC), an organization dedicated to providing educational services and social support for the underprivileged. This organization offers shelter homes for children in need of a safe place to live and drop-in schools for those who need to continue their education. The ETC is also involved in research aimed at advancing science, which led to the author earning the title of Professor and joining the WPF. Additionally, the author is actively involved in global social

Burnout has become a significant occupational concern for resident physicians, primarily attributed to chronic workplace stressors, inadequate work-life balance, high expectation from attending staffs, steep learning curve, and limited patient care experience. The study aimed to investigate the prevalence and associated factors of burnout in medical residents. This cross-sectional study was conducted online questionnaire of all specialists in a university hospital from September to October 2022. Burnout was assessed using the Maslach Burnout Inventory-Human Services Survey for Medical Personnel. The data collection encompassed information on socio-demographics, working conditions, psychiatric issues, and medical errors as potential predictive variables. To analyze the association between these factors and burnout, a confounder summary score model was employed in four separate models utilizing multivariable logistic regression. A total of 238 participants, the average age of participants was 28.1 years (SD 2.7), and 56.2% of them were female. Weekly working hours averaged 75 (SD 21.8). Burnout prevalence was 46.3%. This prevalence was characterized by high levels of emotional exhaustion (57.1%) and depersonalization (36.1%), along with low levels of personal accomplishment (52.4%). Summary of association in each domain with burnout were as follow: demographic determinants, (adjusted odds ratio (aOR) 2.80, 95% CI 1.68–4.64), working conditions (aOR 2.97, 95% CI 1.54–5.71), psychiatric determinants (aOR 2.47, 95% CI 1.77–3.45) medical errors (aOR 2.14, 95% CI 1.05–4.34). Medical residency training programs should provide a supportive system that actively monitors and addresses depressive symptoms. Implementing preventive measures, such as increasing pay rates, can play a role in mitigating burnout.