A nursing home is commonly referred to as a skilled nursing facility, long term care (LTC) facility, or rest home, and may have a different standardized name throughout the United States, but is most commonly referred to as a nursing home. A nursing home traditionally offers 24-hour (skilled) nursing to the elderly or to disabled patients having a variety of medical conditions who require personal care services above that of an assisted living but do not require hospitalization. The personal care services provided may or may not include, but are not limited to: skilled nursing, long term inpatient care, room and board, meals, laundry, and assistance with: dressing, grooming, getting in and out of bed, medications, bathing, and toileting. For purposes of this dataset, an assisted living facility is defined as a facility where the elderly, who are not related to the operator, reside and receive care, treatment, or services. Although not at the level of a nursing home, the services are above the level of an independent living community. They may include several hours per week of supportive care, personal care, or nursing care per resident. Generally, an assisted living facility offers help in daily living (laundry, cooking, cleaning, etc.) and personal assistance (bathing, eating, clothing, etc.). Many assisted living facilities offer assistance with medication and a lesser level of nursing care than what is offered at a nursing home. Assisted living facilities may be regulated by size restrictions depending on which type of assisted living facility it is considered to be in the state in which it exists. For example, Adult Family Homes in Wisconsin have between 3-4 elderly residents while Community Based Residential Facilities have 5 or more. Almost every state has different terminology to describe their version of the assisted living facility system. The structures in which assisted living facilities exist are varied as well. Depending on the type, an assisted living facility may operate out of a personal residence or a nursing home style structure, and it may be set up as apartment style living or as a campus setting in a continuing care retirement community. Multiple assisted living facilities may exist at one location or may be co-located with nursing homes and/or other similar health care facilities. If a facility is licensed by a state and holds multiple licenses, it is represented once in this dataset for each license, even if the licenses are for the same location. This dataset does not include retirement communities, adult daycare facilities, or rehabilitation facilities. Nursing Homes that are operated by and co-located with a hospital are also excluded because the locations are included in the hospital dataset. Records with "-DOD" appended to the end of the [NAME] value are located on a military base, as defined by the Defense Installation Spatial Data Infrastructure (DISDI) military installations and military range boundaries. "#" and "*" characters were automatically removed from standard fields populated by TechniGraphics. Double spaces were replaced by single spaces in these same fields. Text fields in this dataset have been set to all upper case to facilitate consistent database engine search results. All diacritics (e.g., the German umlaut or the Spanish tilde) have been replaced with their closest equivalent English character to facilitate use with database systems that may not support diacritics. The currentness of this dataset is indicated by the [CONTDATE] field. Based on this field, the oldest record dates from 09/22/2009 and the newest record dates from 01/08/2010.

Statistics Canada, in collaboration with the Public Health Agency of Canada and Natural Resources Canada, is presenting selected Census data to help inform Canadians on the public health risk of the COVID-19 pandemic and to be used for modelling analysis. The data provided here show the counts of the population in nursing homes and/or residences for senior citizens by broad age groups (0 to 79 years and 80 years and over) and sex, from the 2016 Census. Nursing homes and/or residences for senior citizens are facilities for elderly residents that provide accommodations with health care services or personal support or assisted living care. Health care services include professional health monitoring and skilled nursing care and supervision 24 hours a day, 7 days a week, for people who are not independent in most activities of daily living. Support or assisted living care services include meals, housekeeping, laundry, medication supervision, assistance in bathing or dressing, etc., for people who are independent in most activities of daily living. Included are nursing homes, residences for senior citizens, and facilities that are a mix of both a nursing home and a residence for senior citizens. Excluded are facilities licensed as hospitals, and facilities that do not provide any services (which are considered private dwellings).

Dataset for the paper assessing the impact of the return of volunteer-led activities on the quality of life of volunteers, residents, and employees of a long-term care institution.

Dresden is a city with a growing number of elderly people. The proportion of people over the age of 60 in the total population is currently about 27 percent. By 2020, it will rise to over 30 percent. In the autumn of life, we think of "reaping the harvest", which means ending a previously busy period of life, but also continuing to participate in the shaping of an active and self-determined life. Designing life - albeit not always independently, but independently - is quality of life into old age. Self-reliance can be achieved through the help offered.

This data set from NYS Office for the Aging (OFA) provides a listing of community resources to help the public find services for older and disabled New Yorkers. Included is information on: AAAs (Area Agencies on Aging), local offices that plan, develop and support comprehensive in-home and community services; HIICAPs (Health Insurance Information Counseling Program) that provide free, accurate and objective information, counseling, assistance and advocacy on Medicare, private health insurance, and related health coverage plans; LTCOP (Long Term Care Ombudsman Program) office resources and advocates for older adults and persons with disabilities who live in nursing homes, assisted living and other licensed adult care homes; and NYConnects, trusted places for information and assistance about long term services and supports whether you are paying for services yourself, through insurance, or eligible for a government program.

Dataset includes README file that describes all datapoints.

This dataset provides Census 2021 estimates that classify usual residents aged 65 years and over living in a care home in England and Wales. The estimates are as at Census Day, 21 March 2021.

Data set on the prevalence of self-care behaviors by non-institutionalized older adults. Personal interviews were conducted with 3,485 individuals 65 years of age and older, with oversampling of the oldest old. Questions were asked about the type and extent of self-care behaviors for activities of daily living, management of chronic conditions (through self-care activities, equipment use, and environmental modifications), medical self-care for acute conditions, health promotion/disease preventions, social support, health service utilization, and socio-demographic/economic status. A follow-up study by telephone was conducted in 1994 to continue examination of subjects. Many of the same questions from the baseline were asked, along with questions regarding change in health status since baseline and nursing home visits. For subjects who had been institutionalized since baseline (Part 2), information was gathered (by proxy) regarding demographic status, living arrangements prior to institutionalization, and reasons for institutionalization. For subjects who had died since baseline (Part 3), information was again gathered through interviews with proxies. Questions covered nursing home admissions and date and place of death. In both waves, a proxy was substituted if the subject was hospitalized (or institutionalized since baseline), too ill, cognitively not able to respond, or deceased. Survey data were linked to Medicare/Medicaid health utilization records. The baseline data are archived at NACDA as ICPSR Study No. 6718, and the followup data are archived as ICPSR Study No. 2592 and linkable to the baseline data. * Dates of Study: 1990-1994 * Study Features: Longitudinal * Sample Size: ** 1990-1: 3,485 (Baseline) ** 1994: 2,601 (Followup) Links: * 1990-1991 Baseline ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06718 * 1994 Follow-up ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/02592

IntroductionThe world’s population is aging at a rapid rate. Nursing homes are needed to care for an increasing number of older adults. Palliative care can improve the quality of life of nursing home residents. Artificial Intelligence can be used to improve palliative care services. The aim of this scoping review is to synthesize research surrounding AI-based palliative care interventions in nursing homes.MethodsA PRISMA-ScR scoping review was carried out using modified guidelines specifically designed for computer science research. A wide range of keywords are considered in searching six databases, including IEEE, ACM, and SpringerLink.ResultsWe screened 3255 articles for inclusion after duplicate removal. 3175 articles were excluded during title and abstract screening. A further 61 articles were excluded during the full-text screening stage. We included 19 articles in our analysis. Studies either focus on intelligent physical systems or decision support systems. There is a clear divide between the two types of technologies. There are key issues to address in future research surrounding palliative definitions, data accessibility, and stakeholder involvement.DiscussionThis paper presents the first review to consolidate research on palliative care interventions in nursing homes. The findings of this review indicate that integrated intelligent physical systems and decision support systems have yet to be explored. A broad range of machine learning solutions remain unused within the context of nursing home palliative care. These findings are of relevance to both nurses and computer scientists, who may use this review to reflect on their own practices when developing such technology.

This dataset is derived from a secondary analysis of a retrospective cohort study conducted in 2024. Participants included those who were aged 65 or older, had stayed in a special nursing home for more than six months before death, were able to take oral intake, and died in the nursing home. Exclusion criteria included deaths outside the nursing home and inability to take oral intake during the study period.The nursing homes were managed by three organizations across different regions in Japan. Data were collected from electronic care files maintained by the nursing homes. The food intake data, visually assessed by care providers, are recorded on a scale of 1 to 10 for each meal component. The average weekly food intake for the 24 weeks leading up to each resident’s death was calculated.

The resources listed here are organizations that provide in-home healthcare for homebound older adults and individuals with advanced illness in need of hospice services or home-based palliative care. Additional resources listed in this section include community-based legal services that support homebound older adults. This type of resource may be necessary so that homebound older adults can complete legal documents to uphold their medical wishes and establish legal surrogates in the case of memory loss or dementia.

BackgroundData on nursing home admission after myocardial infarction (MI) in the elderly are scarce. We investigated nursing home admission within 6 months and 2 years after MI including predictors for nursing home admission in a nationwide cohort of elderly patients.MethodsUsing Danish nationwide registries, we identified all subjects 65 years or older residing at home who were discharged following first-time MI in the period 2008–2015. We determined sex- and age-stratified incidence rates per 1000 person years (IRs) and incidence rate ratios (IRRs) of nursing home admissions using Poisson regression models compared to the Danish population 65 years or older with no prior MI. Poisson regression models were also applied to identify predictors of nursing home admission.ResultsThe 26,539 patients who were discharged after MI had a median age of 76 (quartile 1-quartile 3: 70–83) years. The IRs of nursing home admission after MI increased with increasing age and for 80-84-year-old women IRs after 6 months and 2 years were 113.9 and 62.9, respectively, compared to 29.4 for women of the same age with no prior MI. The IRs for 80-84-year-old men after MI were 56.0 and 36.2, respectively, compared to 24.3 for men of the same age with no prior MI. In adjusted analyses the 6 months and 2 years IRRs for 80-84-year-old subjects were 2.56 (95% CI 2.11–3.10) and 1.41 (95% CI 1.22–1.65) for women and 1.74 (95% CI 1.34–2.25) and 1.05 (95% CI 0.88–1.26) for men, respectively. Predictors were advanced age, dementia, home care, Parkinson’s disease, cerebrovascular disease, living alone, depression, and arrhythmia.ConclusionIn elderly patients discharged following first-time MI, the risk of subsequent nursing home admission within 6 months was 2-fold higher compared to an age-stratified population with no prior MI. After 2 years this risk remained higher in women.

Number of people aged 65 and older who stated "Very safe" or "Pretty safe" to the question "How safe or unsafe does it feel to live in your nursing home?" divided by all people aged 65 and older in nursing homes who answered the survey of older people's perception. No opinion is excluded from the denominator. Data as of 2012. Data are available according to gender breakdown.

analyze the health and retirement study (hrs) with r the hrs is the one and only longitudinal survey of american seniors. with a panel starting its third decade, the current pool of respondents includes older folks who have been interviewed every two years as far back as 1992. unlike cross-sectional or shorter panel surveys, respondents keep responding until, well, death d o us part. paid for by the national institute on aging and administered by the university of michigan's institute for social research, if you apply for an interviewer job with them, i hope you like werther's original. figuring out how to analyze this data set might trigger your fight-or-flight synapses if you just start clicking arou nd on michigan's website. instead, read pages numbered 10-17 (pdf pages 12-19) of this introduction pdf and don't touch the data until you understand figure a-3 on that last page. if you start enjoying yourself, here's the whole book. after that, it's time to register for access to the (free) data. keep your username and password handy, you'll need it for the top of the download automation r script. next, look at this data flowchart to get an idea of why the data download page is such a righteous jungle. but wait, good news: umich recently farmed out its data management to the rand corporation, who promptly constructed a giant consolidated file with one record per respondent across the whole panel. oh so beautiful. the rand hrs files make much of the older data and syntax examples obsolete, so when you come across stuff like instructions on how to merge years, you can happily ignore them - rand has done it for you. the health and retirement study only includes noninstitutionalized adults when new respondents get added to the panel (as they were in 1992, 1993, 1998, 2004, and 2010) but once they're in, they're in - respondents have a weight of zero for interview waves when they were nursing home residents; but they're still responding and will continue to contribute to your statistics so long as you're generalizing about a population from a previous wave (for example: it's possible to compute "among all americans who were 50+ years old in 1998, x% lived in nursing homes by 2010"). my source for that 411? page 13 of the design doc. wicked. this new github repository contains five scripts: 1992 - 2010 download HRS microdata.R loop through every year and every file, download, then unzip everything in one big party impor t longitudinal RAND contributed files.R create a SQLite database (.db) on the local disk load the rand, rand-cams, and both rand-family files into the database (.db) in chunks (to prevent overloading ram) longitudinal RAND - analysis examples.R connect to the sql database created by the 'import longitudinal RAND contributed files' program create tw o database-backed complex sample survey object, using a taylor-series linearization design perform a mountain of analysis examples with wave weights from two different points in the panel import example HRS file.R load a fixed-width file using only the sas importation script directly into ram with < a href="http://blog.revolutionanalytics.com/2012/07/importing-public-data-with-sas-instructions-into-r.html">SAScii parse through the IF block at the bottom of the sas importation script, blank out a number of variables save the file as an R data file (.rda) for fast loading later replicate 2002 regression.R connect to the sql database created by the 'import longitudinal RAND contributed files' program create a database-backed complex sample survey object, using a taylor-series linearization design exactly match the final regression shown in this document provided by analysts at RAND as an update of the regression on pdf page B76 of this document . click here to view these five scripts for more detail about the health and retirement study (hrs), visit: michigan's hrs homepage rand's hrs homepage the hrs wikipedia page a running list of publications using hrs notes: exemplary work making it this far. as a reward, here's the detailed codebook for the main rand hrs file. note that rand also creates 'flat files' for every survey wave, but really, most every analysis you c an think of is possible using just the four files imported with the rand importation script above. if you must work with the non-rand files, there's an example of how to import a single hrs (umich-created) file, but if you wish to import more than one, you'll have to write some for loops yourself. confidential to sas, spss, stata, and sudaan users: a tidal wave is coming. you can get water up your nose and be dragged out to sea, or you can grab a surf board. time to transition to r. :D

Information on residential care services by type of homes, number of homes, number of places

This data set reflects the number of complaints the Long-Term Care (LTC) Ombudsman Program received during Federal Fiscal Years (FFY) 2012, 2013, 2014, 2015, 2016, 2017, 2018, and 2019 on behalf of residents in Residential Care Facilities for the Elderly (RCFE) settings. The LTC Ombudsman Program identifies, investigates and resolves complaints made by or on behalf of residents in LTC facilities and receives and investigates reports of suspected abuse of elder and dependent adults occurring in LTC and some community care facilities. RCFEs include smaller board and care (6 beds) and larger assisted living facilities licensed by the California Department of Social Services. This data corresponds to federally required complaint categories. Complaints that are still open at the end of the FFY would be included in the following FFY data report.

BackgroundRapid global population ageing has significantly increased the number of older adults requiring institutional care. In nursing homes, older residents frequently experience reduced autonomy, diminished social status, and restricted opportunities for meaningful social engagement, all of which can severely threaten their sense of dignity. Although dignity is widely acknowledged as a fundamental human right and is critical for maintaining older adults’ psychological well-being and overall quality of life, limited attention has been paid to understanding how Chinese nursing-home residents perceive, experience, and preserve their dignity. Clarifying these dignity-related experiences is essential to inform interventions and policies aimed at improving care practices and enhancing residents’ quality of life.AimTo address the limited qualitative evidence on dignity in Chinese nursing homes, this study explored residents’ perceptions of dignity, the factors that undermine or enhance it, and the strategies they employ to preserve it.MethodsWe adopted a descriptive phenomenological design to explore nursing-home residents’ perspectives and lived experiences regarding dignity. Between June and December 2023, we conducted semi-structured interviews with 35 nursing-home residents in western China (aged 65–92 years; length of stay 1 to > 7 years). Purposive maximum-variation sampling captured diversity in age, gender, functional status, and socioeconomic background. All interviews were audio-recorded, transcribed verbatim, and thematically analyzed using Colaizzi’s phenomenological procedure. Reporting adhered to SRQR guidelines.ResultsThe following four themes were identified: Older people’s perception of dignity, The influence of dignity on the older people, Factors affecting the promotion of dignity, Dignity maintenance strategies for the older people.ConclusionDignity in nursing-home settings is deeply influenced by physical dependence, the quality of staff–resident interactions, and the availability of meaningful social engagement. When dignity is preserved, residents display better psychosocial adjustment; when violated, they experience significant emotional distress.RecommendationsNursing homes should implement staff training in person-centered, respect-focused care; design routines that maximize privacy and autonomy; and expand social and recreational programs. Future studies should develop and test targeted dignity-enhancement interventions and include family perspectives to create holistic, dignity-oriented care models.

Here are a few use cases for this project:

1. Elderly Care Monitoring: The Fall Detection model can be integrated into smart home systems or camera-assisted monitoring services to promptly identify when elderly individuals fall, enabling caregivers or family members to respond quickly to potential injuries or medical emergencies.

2. Workplace Safety: In high-risk work environments like construction sites or factories, the Fall Detection model can be implemented to monitor employees and detect any accidents, alerting supervisors or emergency medical services immediately to provide assistance.

3. Public Safety: Security cameras in public spaces such as parks, streets, or shopping centers can utilize the Fall Detection model to detect falls and possible criminal activities or accidents, allowing law enforcement or emergency services to respond in a timely manner.

4. Assisted Living Facilities: The Fall Detection model can help improve the safety of residents in assisted living facilities, nursing homes, or rehabilitation centers by monitoring common areas for falls and automatically notifying staff members when incidents occur.

5. Sports Injury Detection: The Fall Detection model can be used in gyms or sports centers to monitor athletes during training sessions, helping to quickly identify falls or injuries and enabling coaches or medical staff to intervene if necessary.

Here are a few use cases for this project:

1. Elderly Care Home Monitoring: The model can be used in elderly care homes or healthcare facilities to observe and monitor the movements and activities of the elderly residents. It can help in tracking whether an elderly person is standing, sitting, or involved in any other activities, and alert the staff if there are any abnormal patterns detected.

2. Home Automation System: The "Tracking Test 2" model can be integrated within smart homes designed particularly for elder populations. Based on whether the occupant is sitting, standing or laying, it can automate certain actions like switch on/off lights, control room temperature, etc., enhancing living convenience.

3. Personal Emergency Alert System: This application can help detect falls or accidents at home, by recognizing an elderly person's sudden change in position, and then alerting medical services or pre-set emergency contacts.

4. Rehabilitation and Physical Therapy: The model can provide assistance in physiotherapy sessions, tracking the movement patterns of elderly patients. It can help therapists in grading the progress and recommending exercises based on their physical state.

5. Assisted Living Technology Development: The model can be used to design and develop new technologies or aids for elderly people, helping them in maintaining their independence. It could be used to create systems that, for example, remind individuals to take medications when they are seated at a certain time, or automated household appliances when they are standing in a certain area.