In 2022, registered nurses in the United States were predominantly white, accounting for over ** percent of all registered nurses. According to the U.S. census, however, roughly ** percent of the U.S. population are white.

The dataset contains estimates for the number of healthcare professionals in 15 different healthcare categories (e.g., Registered Nurse, Dentist, License Clinical Social Worker, etc.) based on completion of license renewal by Race/Ethnicity. There are two timeframes: all current licenses and recent licenses (since 2017). California population estimates are also included to provide a marker for each Race/Ethnicity. Each healthcare professional category can be compared across Race/Ethnicity groups and compared to statewide population estimates, so Race/Ethnicity shortages can be identified for each healthcare professional category. For instance, a notable difference between healthcare professional category and statewide population would indicate either underrepresentation or overrepresentation for that Race/Ethnicity, depending on the direction of the difference.

In 2023, of the over ******* nursing assistants in nursing homes in the United States, roughly **** in ten were white. Meanwhile, Black or African American accounted for another ** percent. Nursing assistants were therefore made up of predominantly racial minorities.

This graph shows the distribution of nursing home residents in the U.S. in 2014, by ethnicity. In the year 2014, around 78 percent of all nursing home residents were white, while 5.3 percent were Hispanic or Latino.

Over 1.3 million people were employed by the NHS in June 2022 and 74.3% of them were white (out of people whose ethnicity was known).

To estimate county of residence of Filipinx healthcare workers who died of COVID-19, we retrieved data from the Kanlungan website during the month of December 2020.22 In deciding who to include on the website, the AF3IRM team that established the Kanlungan website set two standards in data collection. First, the team found at least one source explicitly stating that the fallen healthcare worker was of Philippine ancestry; this was mostly media articles or obituaries sharing the life stories of the deceased. In a few cases, the confirmation came directly from the deceased healthcare worker's family member who submitted a tribute. Second, the team required a minimum of two sources to identify and announce fallen healthcare workers. We retrieved 86 US tributes from Kanlungan, but only 81 of them had information on county of residence. In total, 45 US counties with at least one reported tribute to a Filipinx healthcare worker who died of COVID-19 were identified for analysis and will hereafter be referred to as “Kanlungan counties.” Mortality data by county, race, and ethnicity came from the National Center for Health Statistics (NCHS).24 Updated weekly, this dataset is based on vital statistics data for use in conducting public health surveillance in near real time to provide provisional mortality estimates based on data received and processed by a specified cutoff date, before data are finalized and publicly released.25 We used the data released on December 30, 2020, which included provisional COVID-19 death counts from February 1, 2020 to December 26, 2020—during the height of the pandemic and prior to COVID-19 vaccines being available—for counties with at least 100 total COVID-19 deaths. During this time period, 501 counties (15.9% of the total 3,142 counties in all 50 states and Washington DC)26 met this criterion. Data on COVID-19 deaths were available for six major racial/ethnic groups: Non-Hispanic White, Non-Hispanic Black, Non-Hispanic Native Hawaiian or Other Pacific Islander, Non-Hispanic American Indian or Alaska Native, Non-Hispanic Asian (hereafter referred to as Asian American), and Hispanic. People with more than one race, and those with unknown race were included in the “Other” category. NCHS suppressed county-level data by race and ethnicity if death counts are less than 10. In total, 133 US counties reported COVID-19 mortality data for Asian Americans. These data were used to calculate the percentage of all COVID-19 decedents in the county who were Asian American. We used data from the 2018 American Community Survey (ACS) five-year estimates, downloaded from the Integrated Public Use Microdata Series (IPUMS) to create county-level population demographic variables.27 IPUMS is publicly available, and the database integrates samples using ACS data from 2000 to the present using a high degree of precision.27 We applied survey weights to calculate the following variables at the county-level: median age among Asian Americans, average income to poverty ratio among Asian Americans, the percentage of the county population that is Filipinx, and the percentage of healthcare workers in the county who are Filipinx. Healthcare workers encompassed all healthcare practitioners, technical occupations, and healthcare service occupations, including nurse practitioners, physicians, surgeons, dentists, physical therapists, home health aides, personal care aides, and other medical technicians and healthcare support workers. County-level data were available for 107 out of the 133 counties (80.5%) that had NCHS data on the distribution of COVID-19 deaths among Asian Americans, and 96 counties (72.2%) with Asian American healthcare workforce data. The ACS 2018 five-year estimates were also the source of county-level percentage of the Asian American population (alone or in combination) who are Filipinx.8 In addition, the ACS provided county-level population counts26 to calculate population density (people per 1,000 people per square mile), estimated by dividing the total population by the county area, then dividing by 1,000 people. The county area was calculated in ArcGIS 10.7.1 using the county boundary shapefile and projected to Albers equal area conic (for counties in the US contiguous states), Hawai’i Albers Equal Area Conic (for Hawai’i counties), and Alaska Albers Equal Area Conic (for Alaska counties).20

Scenarios have been used to test assumptions about the evolution of the national quota and the postponement of cessations of activity, in order to measure the impact of the statutory reform of employed nurses in the public hospital or of the pension reform on the evolution of the number of nurses.Several scenarios have been simulated: — the trend scenario is based on the assumption of constant behaviour, namely that the behaviours of nurses observed in a recent past and the measures of regulation of the public authorities would remain unchanged over the whole projection period;- the other scenarios, called variants, differ from the trend scenario only by a hypothesis — corresponding to the evolution of a behaviour or the introduction of a measure — which makes it possible to measure its own impact on the evolution of the workforce.

Demographics includes age, race, gender, geography and other demographic variables describing the study sample.

This dataset tracks annual two or more races student percentage from 2014 to 2023 for Rhode Island Nurses Institute Middle College School District vs. Rhode Island

A. SUMMARY This archived dataset includes data for population characteristics that are no longer being reported publicly. The date on which each population characteristic type was archived can be found in the field “data_loaded_at”.

B. HOW THE DATASET IS CREATED Data on the population characteristics of COVID-19 cases are from:  * Case interviews  * Laboratories  * Medical providers    These multiple streams of data are merged, deduplicated, and undergo data verification processes.  

Race/ethnicity * We include all race/ethnicity categories that are collected for COVID-19 cases. * The population estimates for the "Other" or “Multi-racial” groups should be considered with caution. The Census definition is likely not exactly aligned with how the City collects this data. For that reason, we do not recommend calculating population rates for these groups.

Gender * The City collects information on gender identity using these guidelines.

Skilled Nursing Facility (SNF) occupancy * A Skilled Nursing Facility (SNF) is a type of long-term care facility that provides care to individuals, generally in their 60s and older, who need functional assistance in their daily lives.  * This dataset includes data for COVID-19 cases reported in Skilled Nursing Facilities (SNFs) through 12/31/2022, archived on 1/5/2023. These data were identified where “Characteristic_Type” = ‘Skilled Nursing Facility Occupancy’.

Sexual orientation * The City began asking adults 18 years old or older for their sexual orientation identification during case interviews as of April 28, 2020. Sexual orientation data prior to this date is unavailable. * The City doesn’t collect or report information about sexual orientation for persons under 12 years of age. * Case investigation interviews transitioned to the California Department of Public Health, Virtual Assistant information gathering beginning December 2021. The Virtual Assistant is only sent to adults who are 18+ years old. https://www.sfdph.org/dph/files/PoliciesProcedures/COM9_SexualOrientationGuidelines.pdf">Learn more about our data collection guidelines pertaining to sexual orientation.

Comorbidities * Underlying conditions are reported when a person has one or more underlying health conditions at the time of diagnosis or death.

Homelessness Persons are identified as homeless based on several data sources: * self-reported living situation * the location at the time of testing * Department of Public Health homelessness and health databases * Residents in Single-Room Occupancy hotels are not included in these figures. These methods serve as an estimate of persons experiencing homelessness. They may not meet other homelessness definitions.

Single Room Occupancy (SRO) tenancy * SRO buildings are defined by the San Francisco Housing Code as having six or more "residential guest rooms" which may be attached to shared bathrooms, kitchens, and living spaces. * The details of a person's living arrangements are verified during case interviews.

Transmission Type * Information on transmission of COVID-19 is based on case interviews with individuals who have a confirmed positive test. Individuals are asked if they have been in close contact with a known COVID-19 case. If they answer yes, transmission category is recorded as contact with a known case. If they report no contact with a known case, transmission category is recorded as community transmission. If the case is not interviewed or was not asked the question, they are counted as unknown.

C. UPDATE PROCESS This dataset has been archived and will no longer update as of 9/11/2023.

D. HOW TO USE THIS DATASET Population estimates are only available for age groups and race/ethnicity categories. San Francisco population estimates for race/ethnicity and age groups can be found in a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2016-2020 5-year American Community Survey (ACS).

This dataset includes many different types of characteristics. Filter the “Characteristic Type” column to explore a topic area. Then, the “Characteristic Group” column shows each group or category within that topic area and the number of cases on each date.

New cases are the count of cases within that characteristic group where the positive tests were collected on that specific specimen collection date. Cumulative cases are the running total of all San Francisco cases in that characteristic group up to the specimen collection date listed.

This data may not be immediately available for recently reported cases. Data updates as more information becomes available.

To explore data on the total number of cases, use the ARCHIVED: COVID-19 Cases Over Time dataset.

E. CHANGE LOG

• 9/11/2023 - data on COVID-19 cases by population characteristics over time are no longer being updated. The date on which each population characteristic type was archived can be found in the field “data_loaded_at”.
• 6/6/2023 - data on cases by transmission type have been removed. See section ARCHIVED DATA for more detail.
• 5/16/2023 - data on cases by sexual orientation, comorbidities, homelessness, and single room occupancy have been removed. See section ARCHIVED DATA for more detail.
• 4/6/2023 - the State implemented system updates to improve the integrity of historical data.
• 2/21/2023 - system updates to improve reliability and accuracy of cases data were implemented.
• 1/31/2023 - updated “population_estimate” column to reflect the 2020 Census Bureau American Community Survey (ACS) San Francisco Population estimates.
• 1/5/2023 - data on SNF cases removed. See section ARCHIVED DATA for more detail.
• 3/23/2022 - ‘Native American’ changed to ‘American Indian or Alaska Native’ to align with the census.
• 1/22/2022 - system updates to improve timeliness and accuracy of cases and deaths data were implemented.
• 7/15/2022 - reinfections added to cases dataset. See section SUMMARY for more information on how reinfections are identified.

This dataset tracks annual two or more races student percentage from 2021 to 2022 for Marie Curie High School-nursing vs. New York and New York City Geographic District #10

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. This study examined maternal and youth self-reports of arrests and convictions with official records of crime among participants in three randomized controlled trials of the Nurse-Family Partnership (NFP) in Denver, Colorado, Elmira, New York, and Memphis, Tennessee. Official records were obtained from third-party sources as well as directly from New York State Division of Criminal Justice Services. The collection contains 10 SAS data files: dmom_all.sas7bdat (n=735; 3 variables) dmom_control.sas7bdat (n=247; 26 variables) echild_all.sas7bdat (n=374; 4 variables) echild_control.sas7bdat (n=173; 22 variables) emom_all.sas7bdat (n=399; 4 variables) emom_control.sas7bdat (n=184; 17 variables) mchild_all.sas7bdat (n=708; 5 variables) mchild_control.sas7bdat (n=482; 46 variables) mmom_all.sas7bdat (n=742; 5 variables) mmom_control.sas7bdat (n=514; 25 variables) Demographic variables include race, ethnicity, highest grade completed, household income, marital status, housing density, maternal age, maternal education, husband/boyfriend education, and head of household employment status.

This dataset tracks annual two or more races student percentage from 2011 to 2015 for Hphs Nursing And Health Sciences Academy vs. Connecticut and Hartford School District

High levels of chronic and recurrent workplace stressors can profoundly impact the physical, mental, and emotional health and well-being of the health care workers. Research and interventions specially related to various mindfulness-based interventions have been shown to be beneficial at countering the negative effects of workplace stressors in the healthcare environment. While these interventions have primarily focused on front line healthcare workers, nurse managers have received less attention. In this randomized controlled trial, a sample of nurse managers and assistant nurse managers employed across an academic medical center were randomized into intervention and wait-list control groups. According to their assigned group, they engaged in a commercially available virtual reality mindfulness intervention (TRIPP) during their work day, three times a week for 15 minutes. Over the course of an eight week period, participants in each group engaged with the virtual reality mindfulness in..., Consent and baseline questionnaires, PSS-10, MBI-HSS, CD-10, and UWES-9, for all participants (both Intervention and Wait-List control groups) were completed prior to initiation of the intervention for the Intervention Group, via the participant’s smartphone or computer, accessed via a REDCap link. Participants in the Intervention Group (treatment group) received a VR headset, instructions on use of the VR headset, hand controllers, and the virtual reality mindfulness program; contact information for questions, concerns, or discomfort with the virtual reality technology and software was provided to each participant by a member of the research team. Intervention Group participants were instructed to use the virtual reality mindfulness intervention three times a week during work hours; weekly participation occurred within their worksite office space. At the end of the first eight-week intervention period, all participants (both Intervention and Wait-List Control groups) completed the PSS-..., # Data related to a randomized controlled trial to evaluate the impact of a virtual reality mindfulness intervention for nurse managers in an academic medical center

Dataset DOI: 10.5061/dryad.w6m905r2h

Description of the data and file structure

Title of Dataset: A Randomized Controlled Trial to Evaluate the Impact of a Virtual Reality Mindfulness Intervention for Nurse Managers in an Academic Medical Center

Description of the data and file structure

The csv files contains the study data corresponding to Demographics and Quantitative Outcomes Data. The .R file contains the R Statistical Software code used for analysis of outcomes for PSS-10, MBI-HSS, CD-10, UWES-9

Spreadsheet (1) - Demographic Data (limited to 3 identifiers):Â Spreadsheet_1_S5784AVirtualReality_DATA_2025-10-05_1739_de_identified_DRYAD.csv

• Record ID - Subject Study ID

Demographics:

• Age range- range of participant (in years). Includes ranges 21-30 years, 31-4..., All study participants signed an institutional IRB-approved study consent before participation. The consent included the statement: "If you consent to be in this study, your de-identified information (your survey responses) may be used or shared for future research without your additional consent and may be posted to a public repository for study replication or manuscript publication. Information that could directly identify you will never be included."This submission reflects the raw dataset that includes de-identified demographic indicators (age, race/ethnicity, number of direct reports from REDCap-exported to Excel) for intervention and wait-list control groups of nurse managers and assistant nurse managers who participated in an eight-week worksite virtual reality mindfulness intervention. The responses (number) for each outcome measure question align with the Likert scale range specific to each measure; this de-identified file can be used freely.

Background: Healthcare workers are at high risk for infection from SARS-CoV-2 due to close contact on the front lines. Regardless of infection, these individuals are prone to impacts to their mental health. Aims of the CITF study: The study aimed to determine the extent of participants that had antibodies to SARS-CoV-2 and understand the impact of risk factors on participants’ health during the pandemic. It also aimed to evaluate their mental health status and identify workplace practices and community supports that could be improved to increase safety and alleviate stress (Note: mental health data not shared with CITF Databank [1]). Methods: Healthcare workers (physicians, nurses, health care aids, and personal support workers) across British Columbia, Alberta, Ontario, and Quebec were recruited into a cohort study via advertisements through professional organizations. Participants completed a screening interview and a baseline questionnaire. Participants who tested positive after their blood sample were placed into a nested cohort study. At follow-up visits every 3 months, blood samples were collected, and exposure questionnaires and mental health assessments were administered. A blood sample was also collected 4 months post-vaccine. Contributed dataset contents: The datasets include 3005 participants who completed baseline questionnaires between April 2020 and November 2020. 87% of participants gave one or more blood samples for SARS-CoV-2 serology between September 2020 and June 2022 (in follow-up visits). Variables include data in the following areas of information: demographics (age, gender, race-ethnicity and indigeneity, province, occupation), general health (smokes; asthma, lung disease, or immune compromised diagnosis; height and weight; flu vaccine), and longitudinal follow-up for COVID infections (dates of positive PCR or RAT tests, hospitalization, outcome and scale for impact of infection on everyday life), SARS-CoV-2 vaccination, and serology. [1]: Please contact original study team for mental health data

Financial overview and grant giving statistics of National Coalition of Ethnic Minority Nurse Associations Inc.

The Facility-Level Minimum Data Set (MDS) Frequency dataset provides information for active nursing home residents on topics, such as race/ethnicity, age, or marital status; discharge dispositions; hearing, speech, and vision; cognitive patterns; mood; functional abilities and goals; bladder and bowel; active diagnoses; health conditions; swallowing/nutritional status; oral/dental status; skin conditions; medications; special treatments, procedures, and programs; restraints and alarms; and participation in assessment and goal setting.

Note: The MDS dataset contains more records than most spreadsheet programs can handle. The use of a database or statistical software is generally required. The dataset can be filtered to a more manageable size for use in a spreadsheet program by clicking on the “View Data” button. Additional filter information can be found in the methodology, if needed.

The care delivery team (CDT) is critical to providing care access and equity to patients who are disproportionately impacted by congestive heart failure (CHF). However, the specific clinical roles that are associated with care outcomes are unknown. The objective of this study was to examine the extent to which specific clinical roles within CDTs were associated with care outcomes in African Americans (AA) with CHF. Deidentified electronic medical record data were collected on 5,962 patients, representing 80,921 care encounters with 3,284 clinicians between January 1, 2014 and December 31, 2021. Binomial logistic regression assessed associations of specific clinical roles and the Mann Whitney-U assessed racial differences in outcomes. AAs accounted for only 26% of the study population but generated 48% of total care encounters, the same percentage of care encounters generated by the largest racial group (i.e., Caucasian Americans; 69% of the study population). AAs had a significantly higher number of hospitalizations and readmissions than Caucasian Americans. However, AAs had a significantly higher number of days at home and significantly lower care charges than Caucasian Americans. Among all CHF patients, patients with a Registered Nurse on their CDT were less likely to have a hospitalization (i.e. 30%) and a high number of readmissions (i.e., 31%) during the 7-year study period. When stratified by heart failure phenotype, the most severe patients who had a Registered Nurse on their CDT were 88% less likely to have a hospitalization and 50% less likely to have a high number of readmissions. Similar decreases in the likelihood of hospitalization and readmission were also found in less severe cases of heart failure. Specific clinical roles are associated with CHF care outcomes. Consideration must be given to developing and testing the efficacy of more specialized, empirical models of CDT composition to reduce the disproportionate impact of CHF.

Historical Dataset of Hphs Nursing And Health Sciences Academy is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (2011-2023),Total Classroom Teachers Trends Over Years (2011-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (2011-2023),Asian Student Percentage Comparison Over Years (2011-2023),Hispanic Student Percentage Comparison Over Years (2011-2023),Black Student Percentage Comparison Over Years (2011-2023),White Student Percentage Comparison Over Years (2011-2023),Two or More Races Student Percentage Comparison Over Years (2011-2015),Diversity Score Comparison Over Years (2011-2023),Free Lunch Eligibility Comparison Over Years (2011-2023),Reduced-Price Lunch Eligibility Comparison Over Years (2019-2023),Reading and Language Arts Proficiency Comparison Over Years (2010-2019),Math Proficiency Comparison Over Years (2010-2019),Overall School Rank Trends Over Years (2010-2019),Graduation Rate Comparison Over Years (2011-2019)

Independent living can become challenging for people experiencing cognitive decline. With reduced functioning and greater care needs, many people with dementia (PWD) may need to move to another home with better safety features, move to live closer to or with relatives who can provide care, or enter a nursing home. Housing plays a key role in supporting quality of life for both PWD and their caregivers, so the ability to move when needed is crucial for their well-being. Yet the substantial costs of moving, housing, and care mean that PWD with limited financial resources may be unable to afford moving, exacerbating inequalities between more and less advantaged PWD. Emerging qualitative research considers the housing choices of PWD and their caregivers, yet little is known on a broader scale about the housing transitions PWD actually make over the course of cognitive decline. Prior quantitative research focuses specifically on nursing home admissions; questions remain about how often PWD move to another home or move in with relatives. This study investigates socioeconomic and racial/ethnic disparities in the timing and type of housing transitions among PWD in the United States, using Health and Retirement study data from 2002 through 2016. We find that over half of PWD move in the years around dementia onset (28% move once, and 28% move twice or more) while 44% remain in place. Examining various types of moves, 35% move to another home, 32% move into nursing homes, and 11% move in with relatives. We find disparities by educational attainment and race/ethnicity: more advantaged PWD are more likely to move to another home and more likely to enter a nursing home than less advantaged groups. This highlights the importance of providing support for PWD and their families to transition into different living arrangements as their housing needs change.