This is a fictional dataset created to help the data analysts to play around with the trends and insights on employee jab satisfaction index.

It has the following attributes.

emp_id - Unique ID age - Age Dept - Department location - Employee location education - Employee's education status recruitment_type - Mode of recruitment job_level - 1 to 5. The job level of the employee. 1 being the least and 5 being the highest position rating - 1 to 5. The previous year rating of the employee. 1 being the least and 5 being the highest position onsite - Has the employee ever went to an onsite location? 0 and 1 awards - No. of awards certifications - Is the employee certified? salary - Net Salary satisfied - Is the employee satisfied with his job? Disclaimer: This is purely fictional and does not represent any organization.

This is a study on healthcare workers at the University of North Carolina Hospital system conducted during the COVID-19 pandemic in 2020-2021. This includes responses to survey questions on occupation, living situation, mental health, physical health, prior COVID-19 infection, and vaccination status. As the data are identifiable, we cannot release them publicly. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: These data are owned by the University of North Carolina at Chapel Hill. Contact Dr. Emily Ciccone ciccone@med.unc.edu with inquiries. Format: This dataset includes data on healthcare workers, including questionnaire responses and data from wearable tracking devices. These data are sensitive and participants are potentially identifiable.

United Healthcare Transparency in Coverage Dataset

Unlock the power of healthcare pricing transparency with our comprehensive United Healthcare Transparency in Coverage dataset. This invaluable resource provides unparalleled insights into healthcare costs, enabling data-driven decision-making for insurers, employers, researchers, and policymakers.

Key Features:

• Extensive Coverage: Access detailed pricing information for a wide range of medical procedures and services across the United States, covering approximately 76,000 employers.
• Granular Data: Analyze costs at the provider, plan, and employer levels, allowing for in-depth comparisons and trend analysis.
• Massive Scale: Over 400TB of data generated monthly, providing a wealth of information for comprehensive analysis.
• Historical Perspective: Track pricing changes over time to identify patterns and forecast future trends.
• Regular Updates: Stay current with the latest pricing information, ensuring your analyses are always based on the most recent data.

Detailed Data Points:

For each of the 76,000 employers, the dataset includes: 1. In-network negotiated rates for covered items and services 2. Historical out-of-network allowed amounts and billed charges 3. Cost-sharing information for specific items and services 4. Pricing data for medical procedures and services across providers, plans, and employers

Use Cases

For Insurers: - Benchmark your rates against competitors - Optimize network design and provider contracting - Develop more competitive and cost-effective insurance products

For Employers: - Make informed decisions about health plan offerings - Negotiate better rates with insurers and providers - Implement cost-saving strategies for employee healthcare

For Researchers: - Conduct in-depth studies on healthcare pricing variations - Analyze the impact of policy changes on healthcare costs - Investigate regional differences in healthcare pricing

For Policymakers: - Develop evidence-based healthcare policies - Monitor the effectiveness of price transparency initiatives - Identify areas for potential cost-saving interventions

Data Delivery

Our flexible data delivery options ensure you receive the information you need in the most convenient format:

• Custom Extracts: We can provide targeted datasets focusing on specific regions, procedures, or time periods.
• Regular Reports: Receive scheduled updates tailored to your specific requirements.

Why Choose Our Dataset?

1. Expertise: Our team has extensive experience in healthcare data retrieval and analysis, ensuring high-quality, reliable data.
2. Customization: We can tailor the dataset to meet your specific needs, whether you're interested in particular companies, regions, or procedures.
3. Scalability: Our infrastructure is designed to handle the massive scale of this dataset (400TB+ monthly), allowing us to provide comprehensive coverage without compromise.
4. Support: Our dedicated team is available to assist with data interpretation and technical support.

Harness the power of healthcare pricing transparency to drive your business forward. Contact us today to discuss how our United Healthcare Transparency in Coverage dataset can meet your specific needs and unlock valuable insights for your organization.

Mental Health in the Tech Industry

Exploring Mental Health Conditions in the Tech Industry

By Olga Tsubiks [source]

About this dataset

This dataset contains survey responses from the tech industry about mental health, offering an insightful snapshot into the diagnoses, treatments, and attitudes of those in the field towards mental health. These data points allow people to understand more about how their peers in tech view mental health and can provide greater insight into how to better support those who work in this industry. This dataset includes questions on whether or not respondents have had a mental health disorder or sought treatment for a mental health issue in the past, if they currently have been diagnosed with a condition and what it is, their age group, location of work and residence as well as information on whether they are self-employed or working at a tech company with other questions. Additionally, this dataset also provides insight into respondents' attitudes towards speaking openly about their mental wellbeing versus physical wellbeing. To gain even more understanding of individual's experiences within their place of business overall employee count is included as well what role they fill within that organisation is related to technology/IT. This valuable data set may be used for medical research furthering our knowledge about workplace stressors effecting people seen within this particular field but also across multiple industries to help create support systems that reflect upon individual need rather than one-size fits all models previously employed by employers through out many parts globally

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Research Ideas

• Analyze the correlation between employment industry and mental health status, including self-identified diagnosis, use of mental health services and any history of mental illness in the family.
• Determine if there are differences in how people experience and speak out about their own mental health based on geographic location.
• Compare attitudes towards open conversations on physical vs mental health within different age groups both in the U.S. and abroad

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: OSMI_Survey_Data.csv | Column name | Description | |:-----------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------| | Are you selfemployed | Indicates whether the respondent is self-employed or not. (Boolean) | | How many employees does your company or organization have | Indicates the number of employees in the respondent's company or organization. (Numeric) | | Is your employer primarily a tech companyorganization | Indicates whether the respondent's employer is primarily a tech company or organization. (Boolean) | | Is your primary role within your company related to techIT | Indicates whether the respondent's primary role within their company is related to tech or IT. (Boolean) | | Do you have previous employers | Indicates whether the respondent has had previous employers. (Boolean) ...

The 10,000 Worlds Employee Dataset is a comprehensive dataset designed for analyzing workforce trends, employee performance, and organizational dynamics within a large-scale company setting. This dataset contains information on 10,000 employees, spanning various departments, roles, and experience levels. It is ideal for research in human resource analytics, machine learning applications in employee retention, performance prediction, and diversity analysis.

Key Features of the Dataset: Employee Demographics:

Age, gender, ethnicity Education level, degree specialization Years of experience Employment Details:

Department (e.g., HR, Engineering, Marketing) Job title and seniority level Employment type (full-time, part-time, contract) Performance & Productivity Metrics:

Annual performance ratings Work hours, overtime details Training programs attended Compensation & Benefits:

Salary, bonuses, stock options Benefits (healthcare, pension plans, remote work options) Employee Engagement & Retention:

Job satisfaction scores Attrition and turnover rates Promotion history and career growth Workplace Environment Factors:

Team collaboration metrics Employee feedback and survey results Work-life balance indicators Use Cases: HR Analytics: Identifying patterns in employee satisfaction, retention, and performance. Predictive Modeling: Forecasting attrition risks and promotion likelihoods. Diversity & Inclusion Analysis: Understanding representation across departments. Compensation Benchmarking: Comparing salaries and benefits within and across industries. This dataset is highly valuable for data scientists, HR professionals, and business analysts looking to gain insights into workforce dynamics and improve organizational strategies.

Would you like any additional details or a sample schema for the dataset?

BackgroundDuring the COVID-19 pandemic, many healthcare workers faced extreme working conditions and were at higher risk of infection with the coronavirus. These circumstances may have led to mental health problems, such as anxiety, among healthcare workers. Most studies that examined anxiety among healthcare workers during the COVID-19 pandemic were cross-sectional and focused on the first months of the pandemic only. Therefore, this study aimed to investigate the longitudinal association between working in healthcare and anxiety during a long-term period (i.e., 18 months) of the COVID-19 pandemic.MethodsData were used from online questionnaires of the Lifelines COVID-19 prospective cohort with 22 included time-points (March 2020–November 2021). In total, 2,750 healthcare workers and 9,335 non-healthcare workers were included. Anxiety was assessed with questions from the Mini-International Neuropsychiatric Interview, and an anxiety sum score (0–7) was calculated. Negative binomial generalized estimating equations (GEE), adjusted for demographic, work and health covariates, were used to examine the association between working in healthcare and anxiety.ResultsAnxiety sum scores over time during the COVID-19 pandemic were similar for healthcare workers and non-healthcare workers. No differences between the anxiety sum scores of healthcare workers and non-healthcare workers were found [incidence rate ratio (IRR) = 0.97, 95% CI = 0.91–1.04].ConclusionThis study did not find differences between healthcare workers and non-healthcare in perceived anxiety during the COVID-19 pandemic.

Introduction:

This dataset contains comprehensive information on various demographic, healthcare, and location-related attributes of individuals. The data was collected through a comprehensive survey conducted across diverse geographical locations.

Column Descriptions:

1. Location, _Location_latitude, _Location_longitude, _Location_altitude, _Location_precision: These columns provide the precise geographical coordinates and altitude of the respondents, enabling accurate spatial analysis.

2. Date and Time: This column records the date and time of the survey, providing temporal context for the dataset.

3. Age, Gender, Marital Status, How many children do you have, if any?: These columns capture essential demographic information, including age, gender, marital status, and the number of children, offering insights into the composition of the surveyed population.

4.Employment Status, Monthly Household Income: These attributes provide insights into the financial stability of the respondents, including their employment status and monthly household income. 5. Healthcare-Related Information: a) Have you ever had health insurance? If yes, which insurance cover?: This column identifies respondents with previous health insurance coverage and specifies the type of insurance they had. b) When was the last time you visited a hospital for medical treatment? (In Months): This records the duration, in months, since the respondents' last hospital visit. c) Did you have health insurance during your last hospital visit?: This column indicates whether the respondents had health insurance during their last hospital visit. d) Have you ever had a routine check-up with a doctor or healthcare provider?: This column identifies if respondents have undergone routine health check-ups. e) If you answered yes to the previous question, what time period (in years) do you stay before having your routine check-up?: This captures the time gap, in years, between routine check-ups for respondents. f) Have you ever had a cancer screening (e.g., mammogram, colonoscopy, etc.)?: This column identifies respondents who have undergone cancer screening. g) If you answered yes to the previous question, what time period (in years) do you stay before having your Cancer screening?: This records the time gap, in years, between cancer screenings for respondents.

1. Miscellaneous Information:

a) Your Picture, Your Picture_URL: These columns contain the images and corresponding URLs of the respondents. b) _id, _uuid, _submission_time, _validation_status, _notes, _status, _submitted_by, version, _tags, _index: These are internal identifiers and metadata attributes associated with the dataset.

Use Case:

This dataset can be utilized for various analyses, including demographic profiling, healthcare utilization patterns, and spatial health disparities assessment, thereby facilitating informed policy-making and targeted healthcare interventions.

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within Medical Lake. The dataset can be utilized to gain insights into gender-based income distribution within the Medical Lake population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Medical Lake, among individuals aged 15 years and older with income, there were 1,727 men and 1,670 women in the workforce. Among them, 967 men were engaged in full-time, year-round employment, while 789 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 6.10% fell within the income range of under $24,999, while 4.94% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 7.76% of men in full-time roles earned incomes exceeding $100,000, while 9.63% of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

https://i.neilsberg.com/ch/medical-lake-wa-income-distribution-by-gender-and-employment-type.jpeg" alt="Medical Lake, WA gender and employment-based income distribution analysis (Ages 15+)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Medical Lake median household income by gender. You can refer the same here

BackgroundHospital-acquired infections (HAIs) are significant public health issues, especially in low-and middle-income countries (LMICs). Hand hygiene and low-level disinfection of equipment practices among healthcare workers are some of the essential measures to reduce HAIs. Various infection prevention and control (IPC) interventions to reduce HAI incidence have been developed. However, effective interventions have not been well developed in the LMICs context. Therefore, this protocol aims to develop, pilot, and assess the feasibility and acceptability of an IPC intervention in Cambodia and the Lao People’s Democratic Republic.MethodsThis study will consist of four phases guided by the Medical Research Council (MRC) Framework. Three hospitals will be purposely selected – each from the district, provincial, and national levels – in each country. The gap analysis will be conducted in Phase 1 to explore IPC practices among healthcare workers at each hospital through desk reviews, direct observation of hand hygiene and low-level disinfection of equipment practices, in-depth interviews with healthcare workers, and key informant interviews with stakeholders. In Phase 2, an IPC intervention will be developed based on the results of Phase 1 and interventions selected from a systematic literature review of IPC interventions in LMICs. In Phase 3, the developed intervention will be piloted in the hospitals chosen in Phase 1. In Phase 4, the feasibility and acceptability of the developed intervention will be assessed among healthcare workers and representatives at the selected hospitals. National consultative workshops in both countries will be conducted to validate the developed intervention with the national technical working groups.DiscussionThe MRC Framework will be employed to develop and evaluate an intervention to reduce HAIs in two LMICs. This theoretical framework will be used to explore the factors influencing hand hygiene compliance among healthcare workers. The gap analysis results will allow us to develop a comprehensive IPC intervention to reduce HAI incidence in Cambodia and Lao People’s Democratic Republic. Findings from this protocol will feed into promising IPC interventions to reduce HAI incidence in other resource-limited settings.Clinical trial registrationClinicalTrial.Gov, identifier NCT05547373.

The corona crisis exacerbated social inequalities and hit precarious workers, who, as part of the ‘industrial reserve army’, often cover short-term or seasonal labor demands, particularly hard. While the corona crisis reduced many employment opportunities for precarious workers (e.g., in tourism), healthcare facilities, among others, needed additional labor power. However, the latter work activities were often associated with both increased infection risks and additional burdens due to corresponding protective requirements (e.g., working in protective clothing). Due to few highly demanded employment alternatives, support staff in healthcare facilities faced the dilemma of having to choose between (continuing) employment and protecting their health (Kößler et al., 2023). We expected a particularly strong manifestation of this employment-health dilemma (E-H dilemma) among non-medical workers in healthcare facilities (e.g., cleaners), as the corona crisis simultaneously confronted them with an economic threat (few alternative employment options) and a health threat (a heightened infection risk). Therefore, the project aimed to analyze under which circumstances the combination of an economic and a health threat led to an employment-health dilemma (Study 1). The aim was also to understand how employees coped with economic threats, health threats, and the employment-health dilemma (Study 2). To explore the E-H dilemma, we conducted 42 qualitative interviews with 45 non-medical workers in healthcare facilities. The interviews were based on a semi-structured interview guideline that we developed in a participatory manner with works councils and workers of comparable facilities. Interviews were then transcribed and anonymized. During data cleansing, we removed 6 interviews for methodological reasons (e.g., due to poor audio quality or sampling interviewees outside healthcare facilities) and 9 other interviews for content-related reasons (i.e., interviewees did not report an economic and/or health threat). Then we analyzed the remaining 27 interviews using qualitative content analysis by Mayring (2015). For this purpose, two independent coders inductively formed categories based on interview excerpts that dealt with economic threats, health threats, the EH dilemma (Study 1), and potential coping strategies (Study 2). The analysis of Study 1 showed that the antecedents of economic and health threats can be categorized at a societal, organizational, and personal level. For example, the loss of part-time jobs (societal level), internal organizational restructuring processes (organizational level), and formal training (individual level) contributed to the perception of an economic threat. The perception of a health threat was conditioned, among other things, by the availability of information (societal level), defective protective equipment (organizational level), and contact with people with pre-existing conditions (individual level). Some interviewees who felt the economic threat forced them to keep their employment despite a health threat reported an E-H dilemma. The analysis of Study 2 highlighted that workers used various coping strategies that can be mapped on two axes: On the one hand, these strategies may be either problem-oriented (e.g., naming problems) or emotion-oriented (e.g., cognitively avoiding problems). On the other hand, the mode of coping strategies was either cognitive (e.g., planning work) or behavioral (e.g., reducing stress during leisure activities). Kößler, F. J., Wesche, J. S., & Hoppe, A. (2023). In a no‐win situation: The employment–health dilemma. Applied Psychology, 72(1), 64–84. https://doi.org/10.1111/apps.12393

Success.ai offers a comprehensive, enterprise-ready B2B leads data solution, ideal for businesses seeking access to over 150 million verified employee profiles and 170 million work emails. Our data empowers organizations across industries to target key decision-makers, optimize recruitment, and fuel B2B marketing efforts. Whether you're looking for UK B2B data, B2B marketing data, or global B2B contact data, Success.ai provides the insights you need with pinpoint accuracy.

Tailored for B2B Sales, Marketing, Recruitment and more: Our B2B contact data and B2B email data solutions are designed to enhance your lead generation, sales, and recruitment efforts. Build hyper-targeted lists based on job title, industry, seniority, and geographic location. Whether you’re reaching mid-level professionals or C-suite executives, Success.ai delivers the data you need to connect with the right people.

API Features:

• Real-Time Updates: Our APIs deliver real-time updates, ensuring that the contact data your business relies on is always current and accurate.
• High Volume Handling: Designed to support up to 860k API calls per day, our system is built for scalability and responsiveness, catering to enterprises of all sizes.
• Flexible Integration: Easily integrate with CRM systems, marketing automation tools, and other enterprise applications to streamline your workflows and enhance productivity.

Key Categories Served: B2B sales leads – Identify decision-makers in key industries, B2B marketing data – Target professionals for your marketing campaigns, Recruitment data – Source top talent efficiently and reduce hiring times, CRM enrichment – Update and enhance your CRM with verified, updated data, Global reach – Coverage across 195 countries, including the United States, United Kingdom, Germany, India, Singapore, and more.

Global Coverage with Real-Time Accuracy: Success.ai’s dataset spans a wide range of industries such as technology, finance, healthcare, and manufacturing. With continuous real-time updates, your team can rely on the most accurate data available: 150M+ Employee Profiles: Access professional profiles worldwide with insights including full name, job title, seniority, and industry. 170M Verified Work Emails: Reach decision-makers directly with verified work emails, available across industries and geographies, including Singapore and UK B2B data. GDPR-Compliant: Our data is fully compliant with GDPR and other global privacy regulations, ensuring safe and legal use of B2B marketing data.

Key Data Points for Every Employee Profile: Every profile in Success.ai’s database includes over 20 critical data points, providing the information needed to power B2B sales and marketing campaigns: Full Name, Job Title, Company, Work Email, Location, Phone Number, LinkedIn Profile, Experience, Education, Technographic Data, Languages, Certifications, Industry, Publications & Awards.

Use Cases Across Industries: Success.ai’s B2B data solution is incredibly versatile and can support various enterprise use cases, including: B2B Marketing Campaigns: Reach high-value professionals in industries such as technology, finance, and healthcare. Enterprise Sales Outreach: Build targeted B2B contact lists to improve sales efforts and increase conversions. Talent Acquisition: Accelerate hiring by sourcing top talent with accurate and updated employee data, filtered by job title, industry, and location. Market Research: Gain insights into employment trends and company profiles to enrich market research. CRM Data Enrichment: Ensure your CRM stays accurate by integrating updated B2B contact data. Event Targeting: Create lists for webinars, conferences, and product launches by targeting professionals in key industries.

Use Cases for Success.ai's Contact Data - Targeted B2B Marketing: Create precise campaigns by targeting key professionals in industries like tech and finance. - Sales Outreach: Build focused sales lists of decision-makers and C-suite executives for faster deal cycles. - Recruiting Top Talent: Easily find and hire qualified professionals with updated employee profiles. - CRM Enrichment: Keep your CRM current with verified, accurate employee data. - Event Targeting: Create attendee lists for events by targeting relevant professionals in key sectors. - Market Research: Gain insights into employment trends and company profiles for better business decisions. - Executive Search: Source senior executives and leaders for headhunting and recruitment. - Partnership Building: Find the right companies and key people to develop strategic partnerships.

Why Choose Success.ai’s Employee Data? Success.ai is the top choice for enterprises looking for comprehensive and affordable B2B data solutions. Here’s why: Unmatched Accuracy: Our AI-powered validation process ensures 99% accuracy across all data points, resulting in higher engagement and fewer bounces. Global Scale: With 150M+ employee profiles and 170M veri...

Objective: In general, published studies analyze healthcare utilization, rather than foregone care, among different population groups. The assessment of forgone care as an aspect of healthcare system performance is important because it indicates the gap between perceived need and actual utilization of healthcare services. This study focused on a specific vulnerable group, middle-aged and elderly people with chronic diseases, and evaluated the prevalence of foregone care and associated factors among this population in China. Methods: Data were obtained from a nationally representative household survey of middle-aged and elderly individuals (≥45 years), the China Health and Retirement Longitudinal Study (CHARLS), which was conducted by the National School of Development of Peking University in 2013. Descriptive statistics were used to analyze sample characteristics and the prevalence of foregone care. Andersen's healthcare utilization and binary logistic models were used to evaluate the determinants of foregone care among middle-aged and elderly individuals with chronic diseases. Results: The prevalence of foregone outpatient and inpatient care among middle-aged and elderly people were 10.21% and 6.84%, respectively, whereas the prevalence of foregone care for physical examinations was relatively high (57.88%). Predisposing factors, including age, marital status, employment, education, and family size, significantly affected foregone care in this population. Regarding enabling factors, individuals in the highest income group reported less foregone inpatient care or physical examinations compared with those in the lowest income group. Social healthcare insurance could significantly reduce foregone care in outpatient and inpatient situations; however, these schemes (except for Urban Employee Medical Insurance) did not appear to have a significant impact on foregone care involving physical examinations. Conclusion: In China, policymakers may need to further adjust healthcare policies, such as health insurance schemes, and improve the hierarchical medical system, to promote reduction in foregone care and effective utilization of health services.

This dataset provides the percentage of regular wage/salaried employees in usual status (ps+ss) engaged in the non-agriculture sector who had no written job contract, were not eligible for paid leave, and were not eligible for specified social security benefits. For years before 2017-18, the data was obtained in different quinquennial rounds of NSSO conducted from 2004-05 (NSS 61st) to 2011-12 (NSS 68th round). From 2017-18 the data is sourced from the annual report of the Periodic Labour Force Survey (PLFS) conducted by the Ministry of Statistics and Programme Implementation.

The dataset highlights various conditions of employment, including:

1. No written job contract: Information was recorded in four categories:

No written job contract, Written job contract for 1 year or less, Written job contract for more than 1 year to 3 years, Written job contract for more than 3 years.

1. Not eligible for paid leave: This refers to the absence of leave during sickness, maternity, or other paid leave that an employee is entitled to without loss of pay, excluding paid-off days/holidays typically allowed by an enterprise.

2. Not eligible for social security benefits: This includes employees who were not covered by any of the following social security schemes:

Provident Fund (PF) / pension (GPF, CPF, PPF, etc.), Gratuity, Health care and maternity benefits, Combinations of PF/pension, gratuity, and health care & maternity benefits. Employees not covered under any of these schemes were considered ineligible for social security benefits. This dataset provides a detailed picture of the job security, contractual agreements, and benefits among regular wage/salaried employees in non-agricultural sectors.

Creating a robust employee dataset for data analysis and visualization involves several key fields that capture different aspects of an employee's information. Here's a list of fields you might consider including: Employee ID: A unique identifier for each employee. Name: First name and last name of the employee. Gender: Male, female, non-binary, etc. Date of Birth: Birthdate of the employee. Email Address: Contact email of the employee. Phone Number: Contact number of the employee. Address: Home or work address of the employee. Department: The department the employee belongs to (e.g., HR, Marketing, Engineering, etc.). Job Title: The specific job title of the employee. Manager ID: ID of the employee's manager. Hire Date: Date when the employee was hired. Salary: Employee's salary or compensation. Employment Status: Full-time, part-time, contractor, etc. Employee Type: Regular, temporary, contract, etc. Education Level: Highest level of education attained by the employee. Certifications: Any relevant certifications the employee holds. Skills: Specific skills or expertise possessed by the employee. Performance Ratings: Ratings or evaluations of employee performance. Work Experience: Previous work experience of the employee. Benefits Enrollment: Information on benefits chosen by the employee (e.g., healthcare plan, retirement plan, etc.). Work Location: Physical location where the employee works. Work Hours: Regular working hours or shifts of the employee. Employee Status: Active, on leave, terminated, etc. Emergency Contact: Contact information of the employee's emergency contact person. Employee Satisfaction Survey Responses: Data from employee satisfaction surveys, if applicable.

Code Url: https://github.com/intellisenseCodez/faker-data-generator

IntroductionOrganizational resilience is of paramount importance for coping with adversity, particularly in the healthcare sector during crises. The objective of the present study was to evaluate the impact of resilience-based interventions on the well-being of healthcare employees during the pandemic. In this study, resilience-based interventions are defined as organizational actions that strengthen a healthcare institution’s capacity to cope with crises—such as ensuring adequate personal protective equipment and staff testing, clear risk-communication, alternative care pathways (e.g., telemedicine) and psychosocial support—each mapping onto the recognized resilience capabilities of material resources, information management, collateral pathways and human-capital management The research question focused on two key aspects: first, whether Polish healthcare institutions effectively implemented these interventions, and second, how these interventions were perceived by their employees. The hypothesis tested was that resilience-based interventions positively influence employee well-being.MethodsThe study was conducted between August 21, 2020, and October 6, 2020, in Poland (across all regions). It utilized a cross-sectional, online survey-based approach, targeting healthcare professionals. A 39-item questionnaire was developed and distributed via Microsoft Forms, with participants recruited through websites and newsletters from doctors, nurses, and midwives’ associations. A variety of statistical methods were used to analyze the obtained data, i.e., logistic regression, proportional ordinal logistic regression, multiple marginal independence test, simultaneous pairwise marginal independence test, Cochran Q test, random forest-based imputation of missing data.ResultsThe study found that resilience-based interventions, such as access to personal protective equipment and virus-detection testing, significantly reduced anxiety among healthcare workers. The study indicated a deficiency in employer-provided psychological support. Furthermore, it demonstrated that an increase in workload does not necessarily lead to an increase in employee expectations of recognition and appreciation. Overall, this study underscores the importance of comprehensive managerial strategies in maintaining organizational resilience and improving employee well-being during crises.DiscussionThis study shows that resilience-based management—especially reliable PPE, testing, and clear internal communication—helps protect healthcare workers’ well-being during crises. Strengthening communication and psychological support before future emergencies remains essential. The findings echo existing research and lay groundwork for further work on healthcare resilience and staff well-being.

People with disabilities experience concerning rates of ableism and are significantly under-represented within healthcare education and professions. Focusing on healthcare professions is important for enhancing the diversity and inclusion of equity-deserving groups within the workforce. The objective of this review was to explore the experiences and impact of workplace discrimination and ableism among healthcare providers and trainees with disabilities. Systematic searches of seven databases from 2000 to January 2022 were conducted. Five reviewers independently applied the inclusion criteria, extracted the data and rated the study quality. 48 studies met our inclusion criteria, representing 13,815 participants across six countries over a 21-year period. The findings highlighted rates and types of workplace ableism, which occurred at the institutional (i.e., inaccessible environments, physical barriers and unsupportive work environments) and individual level (i.e., negative attitudes, bullying, harassment). The impact of ableism on healthcare providers included difficulty disclosing due to fear of stigma, and effects on well-being and career development. Our findings revealed a critical need for more research on the experiences of ableism amongst healthcare providers and the impact it has on their well-being. Further efforts should explore mechanisms for including and welcoming people with disabilities in healthcare professions.Implications for rehabilitationWorkplace ableism is prevalent in health care professions and could be discouraging people with disabilities from entering or completing health care education and training, leading to an under-representation of this equity-deserving group within health care.More efforts are needed to recruit, retain and support people with disabilities in the health care workforce.Health care providers who have a disability often experience workplace discrimination and inaccessible physical environments which can impact their health and well-being.Managers, senior leadership and health care organizations should advocate for improved social inclusion of employees with disabilities. Workplace ableism is prevalent in health care professions and could be discouraging people with disabilities from entering or completing health care education and training, leading to an under-representation of this equity-deserving group within health care. More efforts are needed to recruit, retain and support people with disabilities in the health care workforce. Health care providers who have a disability often experience workplace discrimination and inaccessible physical environments which can impact their health and well-being. Managers, senior leadership and health care organizations should advocate for improved social inclusion of employees with disabilities.

Here are a few use cases for this project:

1. Public Health Compliance Monitoring: This model can be integrated into CCTV and surveillance systems in public spaces, businesses, and transit systems to monitor compliance with public health requirements for wearing masks during pandemic situations.

2. Retail and Commercial Business Tracking: Businesses such as shops and malls can use the model to ensure customers are correctly following mask rules. They can use the data collected to identify high-risk periods or locations where compliance is low.

3. Education Institutions: Schools, universities and other educational institutions can use the model to ensure students, staff and visitors are abiding by mask guidelines on campus - both inside buildings and in outdoor areas.

4. Transportation Safety: Airlines, buses, and trains may use this model to monitor passenger compliance with mask regulations during transit. It could also be applied to station or airport security footage.

5. Workplace Safety: In workplaces where masks are mandatory, such as healthcare settings or factories, this model can help monitor employee mask usage and can aid in enforcing proper mask wearing protocol.

Note: This dataset is no longer being updated as of June 2, 2025. This dataset contains numbers of COVID-19 outbreaks and associated cases, categorized by setting, reported to CDPH since January 1, 2021. AB 685 (Chapter 84, Statutes of 2020) and the Cal/OSHA COVID-19 Emergency Temporary Standards (Title 8, Subchapter 7, Sections 3205-3205.4) required non-healthcare employers in California to report workplace COVID-19 outbreaks to their local health department (LHD) between January 1, 2021 – December 31, 2022. Beginning January 1, 2023, non-healthcare employer reporting of COVID-19 outbreaks to local health departments is voluntary, unless a local order is in place. More recent data collected without mandated reporting may therefore be less representative of all outbreaks that have occurred, compared to earlier data collected during mandated reporting. Licensed health facilities continue to be mandated to report outbreaks to LHDs. LHDs report confirmed outbreaks to the California Department of Public Health (CDPH) via the California Reportable Disease Information Exchange (CalREDIE), the California Connected (CalCONNECT) system, or other established processes. Data are compiled and categorized by setting by CDPH. Settings are categorized by U.S. Census industry codes. Total outbreaks and cases are included for individual industries as well as for broader industrial sectors. The first dataset includes numbers of outbreaks in each setting by month of onset, for outbreaks reported to CDPH since January 1, 2021. This dataset includes some outbreaks with onset prior to January 1 that were reported to CDPH after January 1; these outbreaks are denoted with month of onset “Before Jan 2021.” The second dataset includes cumulative numbers of COVID-19 outbreaks with onset after January 1, 2021, categorized by setting. Due to reporting delays, the reported numbers may not reflect all outbreaks that have occurred as of the reporting date; additional outbreaks may have occurred that have not yet been reported to CDPH. While many of these settings are workplaces, cases may have occurred among workers, other community members who visited the setting, or both. Accordingly, these data do not distinguish between outbreaks involving only workers, outbreaks involving only residents or patrons, or outbreaks involving both. Several additional data limitations should be kept in mind: * Outbreaks are classified as “Insufficient information” for outbreaks where not enough information was available for CDPH to assign an industry code. * Some sectors, particularly congregate residential settings, may have increased testing and therefore increased likelihood of outbreak recognition and reporting. As a result, in congregate residential settings, the number of outbreak-associated cases may be more accurate. * However, in most settings, outbreak and case counts are likely underestimates. For most cases, it is not possible to identify the source of exposure, as many cases have multiple possible exposures. * Because some settings have been at times been closed or open with capacity restrictions, numbers of outbreak reports in those settings do not reflect COVID-19 transmission risk. * The number of outbreaks in different settings will depend on the number of different workplaces in each setting. More outbreaks would be expected in settings with many workplaces compared to settings with few workplaces.

The Medical Expenditure Panel Survey (MEPS) Household Component (HC) collects data from a sample of families and individuals in selected communities across the United States, drawn from a nationally representative subsample of households that participated in the prior year's National Health Interview Survey (conducted by the National Center for Health Statistics). During the household interviews, MEPS collects detailed information for each person in the household on the following: demographic characteristics, health conditions, health status, use of medical services, charges and source of payments, access to care, satisfaction with care, health insurance coverage, income, and employment. The panel design of the survey, which features several rounds of interviewing, makes it possible to determine how changes in respondents' health status, income, employment, eligibility for public and private insurance coverage, use of services, and payment for care are related. Public Use Files for Household data are available on the MEPS website.

Morbidity and mortality attributable to COVID-19 is devastating global health systems and economies. Bacillus Calmette Guérin (BCG) vaccination has been in use for many decades to prevent severe forms of tuberculosis in children. Studies have also shown a combination of improved long-term innate or trained immunity (through epigenetic reprogramming of myeloid cells) and adaptive responses after BCG vaccination, which leads to non-specific protective effects in adults. Observational studies have shown that countries with routine BCG vaccination programs have significantly less reported cases and deaths of COVID-19, but such studies are prone to significant bias and need confirmation. To date, in the absence of direct evidence, WHO does not recommend BCG for the prevention of COVID-19. This project aims to investigate in a timely manner whether and why BCG-revaccination can reduce infection rate and/or disease severity in health care workers during the SARS-CoV-2 outbreak in South Africa. These objectives will be achieved with a blinded, randomised controlled trial of BCG revaccination versus placebo in exposed front-line staff in hospitals in Cape Town. Observations will include the rate of infection with COVID-19 as well as the occurrence of mild, moderate or severe ambulatory respiratory tract infections, hospitalisation, need for oxygen, mechanical ventilation or death. HIV-positive individuals will be excluded. Safety of the vaccines will be monitored. A secondary endpoint is the occurrence of latent or active tuberculosis. Initial sample size and follow-up duration is at least 500 workers and 52 weeks. Statistical analysis will be model-based and ongoing in real time with frequent interim analyses and optional increases of both sample size or observation time, based on the unforeseeable trajectory of the South African COVID-19 epidemic, available funds and recommendations of an independent data and safety monitoring board. The study will be supported by a novel 3D lung organoid model of SARS-CoV-2 infection system that can mimic the cascade of immunological events after SARS-CoV-2 infection to determine and analyse the contribution of cellular components to the impact of BCG revaccination in this study. Given the immediate threat of the SARS-CoV-2 epidemic the trial has been designed as a pragmatic study with highly feasible endpoints that can be continuously measured. This allows for the most rapid identification of a beneficial outcome that would lead to immediate dissemination of the results, vaccination of the control group and outreach to the health authorities to consider BCG vaccination for all qualifying health care workers. Methods This dataset was collected in a clinical randomised control trial under the TASK008-BCG CORONA protocol. The trial was conducted in South Africa. This trial was registered with ClinicalTrials.gov, NCT04379336.