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

Retail Sales in the United States increased 0.60 percent in June of 2025 over the previous month. This dataset provides - U.S. December Retail Sales Increased More Than Forecast - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Monthly average retail prices for selected products, for Canada and provinces. Prices are presented for the current month and the previous four months. Prices are based on transaction data from Canadian retailers, and are presented in Canadian current dollars.

Released bi-annually, this data set provides information relating to the total expenditure at each gaming venue for the first 6 months of the 2018/19 financial year. It includes venue classification and the allocation of electronic gaming machines (EGMs) throughout Victoria.

For more information visit the Victorian Commission for Gambling and Liquor Regulation's (VCGLR) website.

Changes in trading name:

• LONDON TAVERN changed name to The CAMDEN TOWN HOTEL in August 2018.

• YALLOURN BOWLING CLUB changed name to NEWBOROUGH BOWLING CLUB in June 2018.

• LEIGHOAK changed name to MVRC LEIGHOAK CLUB in July 2018.

• CITY FAMILY HOTEL changed from Hotel to Club.

• EAST MALVERN RSL ceased trading in October 2018.

• CLUB TIVOLI ceased trading in Novemeber 2018.

Please note:

• AURIN has spatially enabled the original data through joining it with gaming venue locations from the VCGLR's interactive map of Victoria's gaming venues extracted on the 8th of March 2019. Therefore venues that have either changed their trading name or cease to exist as of the 8th of March 2019 will not be included in this dataset, despite being active in the year expenditure was recorded.

• EGM Numbers: Average number of operating EGM's at the gaming venue during the month. This figure is consistent with the average entitlement applied to the EGM as per tax calculation.

The number of Twitter users in the United States was forecast to continuously increase between 2024 and 2028 by in total 4.3 million users (+5.32 percent). After the ninth consecutive increasing year, the Twitter user base is estimated to reach 85.08 million users and therefore a new peak in 2028. Notably, the number of Twitter users of was continuously increasing over the past years.User figures, shown here regarding the platform twitter, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of Twitter users in countries like Canada and Mexico.

The number of Reddit users in the United States was forecast to continuously increase between 2024 and 2028 by in total 10.3 million users (+5.21 percent). After the ninth consecutive increasing year, the Reddit user base is estimated to reach 208.12 million users and therefore a new peak in 2028. Notably, the number of Reddit users of was continuously increasing over the past years.User figures, shown here with regards to the platform reddit, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once. Reddit users encompass both users that are logged in and those that are not.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of Reddit users in countries like Mexico and Canada.