Background and objectivesNurses tend to exhibit higher rates of presenteeism compared to other professions. Presenteeism can cause the work performance of nurses to suffer, jeopardizing their own and their patients’ safety and leading to decreased quality of care and increased risks of errors. However, there is a lack of a validated assessment tool for presenteeism in Taiwan. Thus, the purpose of this study was to develop a Nursing Staff Presenteeism Scale (NSPS).MethodsTo develop questionnaire items, participants from three medical centers in Taiwan were recruited. Through convenience sampling, 500 nurses who met the selection criteria were recruited from November 1, 2022 to January 18, 2023. The scale was developed based on a systematic literature review, a previous study, and expert consultation, and 50 items were initially generated. After removing three items that lacked discriminative power, the reliability and validity of the remaining 47 items were evaluated. An exploratory factor analysis was used to establish the construct validity. A confirmatory factor analysis and structural equation modeling for cross-validation were used to assess relationships of factors with items and the overall NSPS.ResultsThe final scale consisted of 44 items assessed on a five-point Likert scale that loaded onto three different factors of physical or mental discomfort (18 items), work performance (15 items), and predisposing factors (11 items). These three factors were found to explain 63.14% of the cumulative variance. Cronbach’s alpha for the overall final scale was 0.953. The item-to-total correlation coefficients ranged 0.443 to 0.795.ConclusionsThe NSPS exhibited satisfactory reliability and validity. It can be applied to assess the level of presenteeism among clinical nurses and provide medical institutions with information regarding the causes of presenteeism, predisposing factors, and the impacts of presenteeism on their work performance to enhance the safety and quality of clinical care.

Nursing burnout Statistics: Considering the pandemic and post-pandemic time, nursing burnout has become a significant issue in the healthcare industry. We have seen the problems faced by the nurses during the lockdown about they were treated and what kind of exhaustion they faced. But even after 2 years of that event the healthcare industry is still facing the same problem. The major reason behind this problem is the low level of hiring in the nursing segment in healthcare units around the world. These nursing burnout statistics are written with insights from around the globe to understand the severity of the problem. It has included various types of content along with interesting graphics for a better level of understanding. Editor’s Choice In the United States of America, there are around 2.7 million nurses who reported feeling burnout during work in 2022. As of today, Belgium has 60% of the burnout nurses while there are 40% in Uganda. According to Nursing burnout statistics, there are around 81.2% of female nurses and 18.8% of male nurses feel burned out during the sessions of their job. 5% of the nurses in China had suicidal thoughts while 17% of nurses in Australia took mental health support. 6% belonged to the age group of 26 years to 30 years facing the highest number of burned out in all the other age groups. On average today, nursing burnout statistics say that low staffing resulting in 80.19% was the main reason for burnout. 46% and 22% belong to the reasons of ethical dilemmas physical attacks from patients or patients’ families in the United States of America. According to the Nursing burnout statistics, it has been estimated that the world will face a shortage of nurses by the year 2030 resulting in a number of 13 million. As of today, the turnover rate of nurses due to burnout is 27.1%. For every 1% of the turnover in the nursing field, it will cost hospitals around $2,62,300 every year.

Basic information concerning respondents’ demographic characteristics.

In this study, different data sources are mobilized to establish a demographic finding on nurses.- The Adeli directory (Automation of lists): it lists active health professionals, having a legal license to practice their profession. This register is the only exhaustive database of nurses practising in France, which is continuously updated. it also makes it possible to identify the nursing profession. It was enriched by INSEE on the 2006 data, the only data available at the time of this study, in order to distinguish employees in the public hospital from those in the private sector. it covers the entire field of active nurses practising and residing in metropolitan France. It surveys a relatively small number of nurses each year (2 700 in 2008).- The National Inter-Scheme Health Insurance Information System (SNIIR-AM) makes it possible to identify liberal nurses exhaustively.

Introduction and aimsIn the specialized nursing setting, nurses are susceptible to developing negative mental health issues. Such conditions among nurses can potentially result in unfavorable medical outcomes. Consequently, this study aims to explore the role of social support in regulating between sleep and mental health in nurses.MethodsA cross-sectional study was carried out in September 2022 on 1219 nurses in Quanzhou. The study comprised general demographic information and utilized various questionnaires, namely the Social Support Rate Scale (SSRS), Pittsburgh Sleep Quality Index Questionnaire (PSQI), Generalized Anxiety Disorder Questionnaire (GAD-7), and Patient Health Questionnaire-9 (PHQ-9). The data analysis was performed using t-tests, ANOVAs, Pearsons correlations and hierarchical regression analyses in SPSS software.ResultsResults show that significant associations of sleep quality and social support with anxiety and depression. Simple slope analysis shows that under low levels of social support, sleep quality has a positive impact on anxiety(β = 0.598) and depression(β = 0.851), and the impact is significant. Under high levels of social support, sleep quality also has a positive impact on anxiety(β = 0.462) and depression(β = 0.578), but the impact is smaller. This indicates that as the level of social support increases, the positive predictive effect of sleep quality on anxiety and depression gradually diminishes.ConclusionsSocial support has the potential to alter the impact of sleep quality on anxiety and depression. Therefore, healthcare policymakers need to focus on enhancing the level of social support and mitigating the impact of poor sleep on anxiety and depression.

In this study, different data sources are mobilized to establish a demographic finding on nurses.- The Adeli directory (Automation of lists): it lists active health professionals, having a legal license to practice their profession. This register is the only exhaustive database of nurses practising in France, which is continuously updated. it also makes it possible to identify the nursing profession. It was enriched by INSEE on the 2006 data, the only data available at the time of this study, in order to distinguish employees in the public hospital from those in the private sector. it covers the entire field of active nurses practising and residing in metropolitan France. It surveys a relatively small number of nurses each year (2 700 in 2008).- The National Inter-Scheme Health Insurance Information System (SNIIR-AM) makes it possible to identify liberal nurses exhaustively.

In 2020, nursing home residents in the United States were mostly *****, ************, ****** and over the age of ** years. The gender distribution was roughly six women to four men. Despite a ***** of residents being over 85 years, some ** percent were under the age of 65 years.

The 1985 National Nursing Home Survey was designed to gather a variety of data on all types of nursing homes providing nursing care in the United States. In this collection data are available on nursing and related care facilities, services provided by the facilities, residents of the nursing homes, and discharges. Nursing home care is examined from the perspectives of both the recipients and the providers of services. Information about patients, both current and discharged, includes basic demographic characteristics, marital status, place of residence prior to admission, health status, services received, and, for discharges, the outcomes of care. A family member of both current and discharged patients was contacted by telephone to obtain data on socioeconomic status and prior episodes of health care. Facility-level data include basic characteristics such as size, ownership, Medicare/Medicaid certification, occupancy rate, and days of care provided.

The All CMS Data Feeds dataset is an expansive resource offering access to 119 unique report feeds, providing in-depth insights into various aspects of the U.S. healthcare system including nursing facility owners and accountable care organization participants contact data. With over 25.8 billion rows of data meticulously collected since 2007, this dataset is invaluable for healthcare professionals, analysts, researchers, and businesses seeking to understand and analyze healthcare trends, performance metrics, and demographic shifts over time. The dataset is updated monthly, ensuring that users always have access to the most current and relevant data available.

Dataset Overview:

118 Report Feeds: - The dataset includes a wide array of report feeds, each providing unique insights into different dimensions of healthcare. These topics range from Medicare and Medicaid service metrics, patient demographics, provider information, financial data, and much more. The breadth of information ensures that users can find relevant data for nearly any healthcare-related analysis. - As CMS releases new report feeds, they are automatically added to this dataset, keeping it current and expanding its utility for users.

25.8 Billion Rows of Data:

• With over 25.8 billion rows of data, this dataset provides a comprehensive view of the U.S. healthcare system. This extensive volume of data allows for granular analysis, enabling users to uncover insights that might be missed in smaller datasets. The data is also meticulously cleaned and aligned, ensuring accuracy and ease of use.

Historical Data Since 2007: - The dataset spans from 2007 to the present, offering a rich historical perspective that is essential for tracking long-term trends and changes in healthcare delivery, policy impacts, and patient outcomes. This historical data is particularly valuable for conducting longitudinal studies and evaluating the effects of various healthcare interventions over time.

Monthly Updates:

• To ensure that users have access to the most current information, the dataset is updated monthly. These updates include new reports as well as revisions to existing data, making the dataset a continuously evolving resource that stays relevant and accurate.

Data Sourced from CMS:

• The data in this dataset is sourced directly from the Centers for Medicare & Medicaid Services (CMS). After collection, the data is meticulously cleaned and its attributes are aligned, ensuring consistency, accuracy, and ease of use for any application. Furthermore, any new updates or releases from CMS are automatically integrated into the dataset, keeping it comprehensive and current.

Use Cases:

Market Analysis:

• The dataset is ideal for market analysts who need to understand the dynamics of the healthcare industry. The extensive historical data allows for detailed segmentation and analysis, helping users identify trends, market shifts, and growth opportunities. The comprehensive nature of the data enables users to perform in-depth analyses of specific market segments, making it a valuable tool for strategic decision-making.

Healthcare Research:

• Researchers will find the All CMS Data Feeds dataset to be a robust foundation for academic and commercial research. The historical data, combined with the breadth of coverage across various healthcare metrics, supports rigorous, in-depth analysis. Researchers can explore the effects of healthcare policies, study patient outcomes, analyze provider performance, and more, all within a single, comprehensive dataset.

Performance Tracking:

• Healthcare providers and organizations can use the dataset to track performance metrics over time. By comparing data across different periods, organizations can identify areas for improvement, monitor the effectiveness of initiatives, and ensure compliance with regulatory standards. The dataset provides the detailed, reliable data needed to track and analyze key performance indicators.

Compliance and Regulatory Reporting:

• The dataset is also an essential tool for compliance officers and those involved in regulatory reporting. With detailed data on provider performance, patient outcomes, and healthcare utilization, the dataset helps organizations meet regulatory requirements, prepare for audits, and ensure adherence to best practices. The accuracy and comprehensiveness of the data make it a trusted resource for regulatory compliance.

Data Quality and Reliability:

The All CMS Data Feeds dataset is designed with a strong emphasis on data quality and reliability. Each row of data is meticulously cleaned and aligned, ensuring that it is both accurate and consistent. This attention to detail makes the dataset a trusted resource for high-stakes applications, where data quality is critical.

Integration and Usability:

Ease of Integration:

• The dataset is provided in a CSV format, which is widely compatible with most data analysis too...

The National Medical Expenditure Survey (NMES) series provides information on health expenditures by or on behalf of families and individuals, the financing of these expenditures, and each person's use of services. The Institutional Population Component (IPC) is a survey of nursing and personal care homes and facilities for the mentally retarded and residents admitted to those facilities. Information was collected on facilities and their residents at several points during 1987. Use and expenditure estimates for institutionalized persons can be combined with those from the Household component for composite estimates covering most of the civilian population. Information on facilities and residents was collected from facility administrators and caregivers, with additional information collected from next of kin or other knowledgeable respondents. These data were supplemented by Medicare claims information for covered sample persons. Public Use Tape 17 is the first release of expenditure and use data from the IPC. It provides demographic information such as race, age, sex, education, veteran status, medical history, income, family, date of admission, vital status, residence history, use of long-term care, insurance coverage, and home ownership. Additional information covers the respondent's institutional stays in 1987, dates and lengths of stays, and characteristics of the institution, including size, type, ownership, and certification status. Also provided are data on expenses and sources of payments for services rendered in nursing and personal care homes.

About Dataset

Context:

This dataset simulates patient data from a hospital in the United Arab Emirates (UAE), focusing on diabetes-related diagnoses. It includes demographic information, visit details, and healthcare service times, along with intentional data quality issues such as missing values, duplicates, and inconsistencies. The dataset is designed to reflect real-world healthcare scenarios, making it suitable for practicing data cleaning, analysis, and predictive modeling.

Inspiration:

The dataset was inspired by the need for realistic healthcare data that can be used for training and testing in data science and machine learning. It aims to provide a comprehensive and challenging dataset for learners and professionals to explore healthcare analytics, predictive modeling, and data preprocessing techniques.

Dataset Information:

• Size: 100,000 rows and 13 columns.
• Columns:
  • Visit_Date: Date of the patient's visit (past 2 years).
  • Patient_ID: Unique identifier for each patient (with duplicates).
  • Age: Patient age (0–100 years).
  • Gender: Patient gender (Male, Female, Other, or missing).
  • Diagnosis: Diabetes-related diagnosis (Type 1, Type 2, Prediabetes, Gestational, or missing).
  • Has_Insurance: Insurance status (Yes, No, or missing).
  • Total_Cost: Total cost of the visit in AED (with some invalid negative values).
  • Region: Emirate where the patient is located (e.g., Abu Dhabi, Dubai).
  • Area: Specific location within the emirate (e.g., Al Ain, Palm Jumeirah).
  • Registration time: Time spent during registration (in minutes).
  • Nursing time: Time spent with nursing staff (in minutes).
  • Laboratory time: Time spent in the laboratory (in minutes).
  • Consultation time: Time spent in consultation (in minutes).
  • Pharmacy time: Time spent at the pharmacy (in minutes).

Usage Scenarios:

This dataset can be utilized for a wide range of purposes, including: - Developing and testing healthcare predictive models: Predict diabetes types or patient outcomes based on demographic and visit data. - Practicing data cleaning, transformation, and analysis techniques: Handle missing values, duplicates, and inconsistencies. - Creating data visualizations: Gain insights into healthcare trends, such as the distribution of diabetes types across regions or age groups. - Learning and teaching data science and machine learning concepts: Use the dataset to teach classification, regression, and clustering techniques in a healthcare context.

You can treat it as a Multi-Class Classification Problem and solve it for Diagnosis, which contains 4 categories: - Type 1 Diabetes - Type 2 Diabetes - Prediabetes - Gestational Diabetes

Acknowledgments:

This dataset was created synthetically to mimic real-world healthcare data. Special thanks to the UAE postal code and geographic information used to structure the Region and Area columns.

Image Credit:

Image by [Walid Barghout].

Introduction and aimsIn the specialized nursing setting, nurses are susceptible to developing negative mental health issues. Such conditions among nurses can potentially result in unfavorable medical outcomes. Consequently, this study aims to explore the role of social support in regulating between sleep and mental health in nurses.MethodsA cross-sectional study was carried out in September 2022 on 1219 nurses in Quanzhou. The study comprised general demographic information and utilized various questionnaires, namely the Social Support Rate Scale (SSRS), Pittsburgh Sleep Quality Index Questionnaire (PSQI), Generalized Anxiety Disorder Questionnaire (GAD-7), and Patient Health Questionnaire-9 (PHQ-9). The data analysis was performed using t-tests, ANOVAs, Pearsons correlations and hierarchical regression analyses in SPSS software.ResultsResults show that significant associations of sleep quality and social support with anxiety and depression. Simple slope analysis shows that under low levels of social support, sleep quality has a positive impact on anxiety(β = 0.598) and depression(β = 0.851), and the impact is significant. Under high levels of social support, sleep quality also has a positive impact on anxiety(β = 0.462) and depression(β = 0.578), but the impact is smaller. This indicates that as the level of social support increases, the positive predictive effect of sleep quality on anxiety and depression gradually diminishes.ConclusionsSocial support has the potential to alter the impact of sleep quality on anxiety and depression. Therefore, healthcare policymakers need to focus on enhancing the level of social support and mitigating the impact of poor sleep on anxiety and depression.

This study aimed to investigate the safety attitudes of general practice nurses (GPNs). Data analysis was conducted to examine the effects of demographic factors—such as length of work experience and the number of general practitioners (GPs) or GPNs in the practice—on safety attitudes.

The findings indicated a positive relationship between safety attitudes and length of work experience and a negative relationship between safety attitudes and the number of GPs or GPNs in the practice. These relationships were measured using independent sample t-tests, ANOVA, and the Kruskal-Wallis H test.

Additionally, open-ended questions were employed to identify the safety-related concerns faced by GPNs and their needs regarding current practices. Data collection was conducted via a questionnaire comprising 34 items on a 5-point Likert scale and 8 open-ended questions. Quantitative data were analysed using SPSS, whilst qualitative data were analysed using NVivo.

One of the authors, a practising GPN, facilitated participant recruitment through professional connections. Invitations and survey links were distributed via designated gatekeepers.

This dataset supports the New York State Department of Health Nursing Home Profile public website. The dataset includes facility demographic information, inspection results, and complaint summary and state enforcement fine data. Visit the Nursing Home Profile website at: https://profiles.health.ny.gov/nursing_home/

Introduction

Nursing Burnout Statistics: Nursing burnout has emerged as a significant global concern, characterized by emotional exhaustion, depersonalization, and a diminished sense of personal accomplishment. A 2023 meta-analysis encompassing 94 studies reported a global prevalence of nursing burnout at 30%, with variations across regions and specialties.

In the United States, a 2020 survey indicated that nearly 62% of nurses experienced burnout, with the rate rising to 69% among those under 25 years old. Similarly, a 2023 study found that 91.1% of nurses reported high levels of burnout, compared to 79.9% among other healthcare workers.

Contributing factors to this phenomenon include understaffing, extended work hours, and high patient-to-nurse ratios. The American Nurses Foundation reported in 2023 that 56% of nurses experienced burnout, with 64% feeling significant job-related stress. Moreover, 40% of nurses felt they had poor control over their workload, describing their daily work as hectic or intense.

Addressing nursing burnout necessitates systemic changes, including improved staffing, supportive work environments, and accessible mental health resources. Implementing such measures is crucial to safeguard both healthcare providers and patients.

The National Health and Nutrition Examination Survey I Epidemiologic Followup Study (NHEFS) is a longitudinal study that follows participants from the NHANES I who were aged 25-74 in 1971-1975. The NHEFS surveys were designed to investigate the association between factors measured at the baseline and the development of specific health conditions and functional limitations. Follow-up data were collected in 1982-1984 (ICPSR 8900), 1986 (ICPSR 9466), 1987 (ICPSR 9854), and 1992. The 1992 NHEFS collected information on changes in the health and functional status of the NHEFS cohort since the last contact period. The Vital and Tracing Status file (Part 1) provides summary information about the status of the NHEFS cohort. The Interview Data file (Part 2) covers selected aspects of the respondent's health history, including injuries, activities of daily living, vision and hearing, medical conditions, exercise, weight, family history of cancer, surgeries, smoking, alcohol use, and medical care utilization. The Health Care Facility Stay files (Parts 3 and 4) supply information about stays in hospitals, nursing homes, and mental health care facilities, as well as information abstracted from facility medical records. The Mortality Data file (Part 5) contains data abstracted from the death certificates for NHEFS decedents.

The National Medical Expenditure Survey (NMES) series provides information on health expenditures by or on behalf of families and individuals, the financing of these expenditures, and each person's use of services. The Institutional Population Component (IPC) is a survey of nursing and personal care homes and facilities for the mentally retarded and residents admitted to those facilities. Information was collected on facilities and their residents at several points during 1987. Use and expenditure estimates for institutionalized persons can be combined with those from the Household Component for composite estimates covering most of the civilian population. Information on facilities and residents was collected from facility administrators and caregivers, with additional information collected from next-of-kin or other knowledgeable respondents. These data were supplemented by Medicare claims information for covered sample persons. Research File 36 provides information from the Medicare Automated Data Retrieval System (MADRS) for a subset of persons from File 1 of NATIONAL MEDICAL EXPENDITURE SURVEY, 1987: INSTITUTIONAL POPULATION COMPONENT, FACILITY USE AND EXPENDITURE DATA FOR NURSING AND PERSONAL CARE HOME RESIDENTS PUBLIC USE TAPE 17 and a subset of persons from File 1 of NATIONAL MEDICAL EXPENDITURE SURVEY, 1987: INSTITUTIONAL POPULATION COMPONENT, FACILITY USE AND EXPENDITURE DATA FOR RESIDENTS OF FACILITIES FOR PERSONS WITH MENTAL RETARDATION RESEARCH FILE 22R. Six data files are provided for Research File 36R, all of which contain demographic data such as age, sex, and race. Other variables common to all parts are facility type, person number, sample person identifier, reimbursement amount by Medicare, and total charges reported by provider. Parts 1-6 cover, respectively, Part B Payment Records, Part B Outpatient Bill Records, Part B Home Health Bill Records, Part A Inpatient/Skilled Nursing Facilities Bill Records, Part A Home Health Bill Records, and Part A Hospice Bill Records.

This registry provides a list of licensed nursing facilities in Connecticut as of September 30th each year, beginning with data from 2013. For each facility, this dataset includes aggregate resident demographic information, private pay rates for private and semi-private rooms, payment sources and occupancy levels for each year.

The 2004 National Nursing Home Survey (NNHS), conducted between August and December of 2004, was reintroduced into the field after a five-year break, during which time the survey was redesigned and expanded to collect many new data items. All nursing homes that participated in the NNHS had at least three beds and were either certified (by Medicare or Medicaid) or had a state license to operate as a nursing home. The redesigned survey was administered using a computer-assisted personal interviewing (CAPI) system and included a supplemental survey of nursing assistants employed by nursing homes, the National Nursing Assistant Survey (NNAS), which was sponsored by the Office of the Assistant Secretary for Planning and Evaluation (APSE).

The National Nursing Home Survey provides information on nursing homes from two perspectives-that of the provider of services and that of the recipient of care. Data about the facilities include characteristics such as size, ownership, Medicare/Medicaid certification, services provided and specialty programs offered, and charges. For recipients, data were obtained on demographic characteristics, health status and medications taken, services received, and sources of payment.

Data for the survey were obtained through personal interviews with facility administrators and designated staff who used administrative records to answer questions about the facilities, staff, services and programs, and medical records to answer questions about the residents.

The total number of nursing home facilities that participated in NNHS is 1,174 and the total number of nursing assistants that participated in the National Nursing Assistant Survey is 3,017.