Long-term Care For Older Adults Research Articles

Attitudes Towards Filial Responsibility in 11 European Countries

This study examines how attitudes towards filial responsibility (AFR) have changed in 11 European countries between 2001 and 2017, based on data from the International Social Survey Programme. These countries include various types of welfare states and family traditions. The study also analyses the change in AFR according to the respondents’ gender and age. The findings indicate that in 2017, individuals reported lower filial responsibility than in 2001, with the exception of Great Britain, where the AFR increased. The most substantial decreases in AFR were observed in Hungary, France, Denmark, and Finland. This negative shift is visible in both genders and all age groups, particularly late middle-aged women. However, despite the varying intensity of AFR change, it was challenging to identify clear patterns in the variations between countries. These results highlight potential negative effects on political proposals for long-term care for older adults supported by younger generations.

Data science techniques to gain novel insights into quality of care: a scoping review of long-term care for older adults

Background: The increase in powerful computers and technological devices as well as new forms of data analysis such as machine learning have resulted in the widespread availability of data science in healthcare. However, its role in organizations providing long-term care (LTC) for older people LTC for older adults has yet to be systematically synthesized. This analysis provides a state-of-the-art overview of 1) data science techniques that are used with data accumulated in LTC and for what specific purposes and, 2) the results of these techniques in researching the study objectives at hand. Methods: A scoping review based on guidelines of the Joanna Briggs Institute. PubMed and Cumulative Index to Nursing and Allied Health Literature (CINAHL) were searched using keywords related to data science techniques and LTC. The screening and selection process was carried out by two authors and was not limited by any research design or publication date. A narrative synthesis was conducted based on the two aims. Results: The search strategy yielded 1,488 studies: 27 studies were included of which the majority were conducted in the US and in a nursing home setting. Text-mining/natural language processing (NLP) and support vector machines (SVMs) were the most deployed methods; accuracy was the most used metric. These techniques were primarily utilized for researching specific adverse outcomes including the identification of risk factors for falls and the prediction of frailty. All studies concluded that these techniques are valuable for their specific purposes. Discussion: This review reveals the limited use of data science techniques on data accumulated in or by LTC facilities. The low number of included articles in this review indicate the need for strategies aimed at the effective utilization of data with data science techniques and evidence of their practical benefits. There is a need for a wider adoption of these techniques in order to exploit data to their full potential and, consequently, improve the quality of care in LTC by making data-informed decisions.

The impact of social support for older adults in nursing homes on successful aging: a moderated mediation model.

To investigate the connection between social support (SS) and successful aging (SA) in older adults residing in nursing homes, examining the mediating role of meaning in life (MIL). Additionally, this study aims to assess whether frailty moderates the mediation model. A cross-sectional survey approach was employed to recruit older adults from six nursing homes in Sichuan Province between August 2022 and December 2022. Questionnaires, including the General Information Questionnaire, Social Support Rating Scale (SSRS), Meaning in Life Questionnaire (MLQ), Tilburg Frailty Indicator (TFI), and Successful Aging Inventory (SAI), were administered. Data obtained from the completed questionnaires were analyzed using SPSS and its macro program PROCESS. SS emerged as a noteworthy positive predictor of SA in older adults of nursing homes. MIL was identified as a partial mediator in the link between SS and SA. Furthermore, frailty attenuated the positive predictive impact of MIL on SA and moderated the latter part of the mediation model, wherein SS influences SA through MIL. The influence of MIL on SA was more pronounced in older adults with lower frailty levels in nursing homes, while it was diminished in those with higher levels of frailty. Apart from ensuring the availability of essential medical resources in long-term care for older adults, workers in nursing homes should also recognize the significance of "spiritual aging" to cultivate a sense of MIL among older adults. Simultaneously, attention must be directed toward screening for frailty indicators in older adults. Psychological care and physical exercise programs should be intensified for older adults with a high level of frailty, aiming to decelerate the progression of frailty in nursing home residents. This approach leverages the mediating role of MIL and the moderating influence of frailty, ultimately enhancing SA and promoting healthy aging in older adults within nursing home settings.

Tensions between real-world practices and the digitalization paradigm for data-driven services in eldercare: observations from an ethnographic study in Sweden

BackgroundThe implementation of a data-driven approach within the health care system happens in a rapid pace; including in the eldercare sector. Within Swedish eldercare, data-driven health approach is not yet widely implemented. In the specific context of long-term care for older adults, quality of care is as much determined by how social care is being performed as it is by what kind medical care that is provided. In particular, relational aspects have been proven to have a crucial influence on the experience of quality of care for the actors involved. Drawing on ethnographic material collected at a Swedish nursing home, this paper explores in what way the relational aspects of care could potentially become affected by the increased use of a data-driven health approach.MethodsAn ethnographic approach was adopted in order to investigate the daily care work at a long-term care facility as it unfolded. Fieldwork was conducted at a somatic ward in a Swedish long-term care facility over 4 months (86 h in total), utilizing the methods of participant observation, informal interviews and document analysis. The material was analyzed iteratively throughout the entire research process adopting thematic analysis.ResultsViewing our ethnographic material through an observational lense problematising the policy discourse around data-driven health approach, two propositions were developed. First, we propose that relational knowledge risk becoming less influential in shaping everyday care, when moving to a data-driven health approach. Second, we propose that quality of care risk becoming more directed on quality of medical care at the expense of quality of life.ConclusionWhile the implementation of data-driven health approach within long-term care for older adults is not yet widespread, the general development within health care points towards a situation in which this will become reality. Our study highlights the importance of taking the relational aspects of care into consideration, both during the planning and implementation phase of this process. By doing this, the introduction of a data-driven health approach could serve to heighten the quality of care in a way which supports both quality of medical care and quality of life.

Smart Use of Data Improves Quality of Long-Term Care for Older Adults

Smart Use of Data Improves Quality of Long-Term Care for Older Adults

RECONSTRUCTING PERSON-CENTEREDNESS FROM A SYSTEMS PERSPECTIVE: IMPLICATIONS FOR THE CARE OF OLDER ADULTS

Abstract Person-centeredness has become a common, almost hackneyed phrase in the context of care for older adults, despite a lack of consensus regarding its definition, measurement, and application. As part of a series of studies aimed at better understanding person-centeredness, we sought to identify key themes about person-centeredness as it applies to the system (macro) level, as opposed to the setting (meso) and interpersonal (micro) levels. Four think-tank meetings (two in-person; two virtual) were held and results analyzed; the 32 participants included researchers, providers, policy makers, ethicists, and consumers. Think-tank meeting prompts addressed a range of systems, including education, government, workplace, and healthcare. Analyses identified themes associated with variation in person-centeredness. Five key themes emerged: (1) The goals and manifestation of person-centeredness vary depending on the type of system, level within a system, and the role of the individual within the system. (2) Principles of person-centeredness from the hospitality industry can be applied to other settings, including healthcare and long-term care. (3) Some limits on person-centeredness are necessary for system success. (4) The amount of person-centeredness provided in a setting depends on available resources such as time and money. (5) Societal inequities, such as discrimination, bias, and language barriers, hinder person-centeredness. Presentation of results will draw parallels between a variety of systems, demonstrating universal applicability, including to healthcare and long-term care for older adults. The themes identified may help guide future policy and practice regarding realistic and effective application and evaluation of person-centeredness in relation to the care of older adults.

A GOLD MINE OF TEXT TEXT MINING VERSUS MANUAL CODING METHODS REGARDING QUALITY OF CARE IN LONG-TERM CARE FOR OLDER ADULTS

Abstract In long-term care for older adults, large amounts of text are collected that cover the quality of care, such as transcribed interviews, medical records, and text in electronic health records. Researchers currently analyze textual data manually to gain insights, which is a time-consuming process. Text mining (TM) could provide a solution, as this methodology can analyze large amounts of text automatically. Therefore, this study aims to compare TM to the gold standard of manual coding regarding sentiment analysis and thematic content analysis. Data was collected from interviews with residents (n=21), family (n=20) and care professionals (n=20). TM models were developed and compared to the manual approach. The results of the manual and TM approach were evaluated in three ways: accuracy, consistency and expert feedback. Accuracy showed how similar the approaches are. Consistency showed if an individual approach finds the same themes in similar text segments. Expert feedback was used to show the perceived correctness of the TM approach. The accuracy analysis showed that 81.8% of text segments receive the same sentiment code in both approaches and 83.7% of text segments receive the same thematic codes. Interviews coded by TM had a higher consistency compared to those coded manually. Expert feedback showed that the TM had a limited understanding of the context. However, it also showed certain inconsistencies of manual coding. The current study has shown that TM can be an effective tool for quickly and accurately identifying sentiment and thematic content in large amounts of textual data.

DATA SCIENCE TECHNIQUES TO GAIN NOVEL INSIGHTS ON QUALITY OF CARE: A SCOPING REVIEW OF LONG-TERM CARE FOR OLDER ADULTS

Abstract The increase in powerful computers and technological devices as well as new forms of data analysis such as machine learning have resulted in the widespread availability of data science in healthcare. However, its role in organisations providing long-term care for older adults (LTC) has yet to be systematically synthesized. This study provides a state-of-the-art scoping overview of (1) data science techniques that are used with data accumulated in LTC and for what specific purposes, and (2) the results of employing these techniques in researching the study objective. PubMed and CINHAL was searched using keywords on data science techniques and LTC. The screening-and selection process was carried out by two authors and not limited by research design or publication date. A narrative synthesis was conducted based on the two aims. The search strategy yielded 1,277 studies; 18 were included (the majority conducted in the US and in nursing homes). Machine learning models/algorithms were the most used techniques and were primarily used for researching specific adverse outcomes including the identification of risk factors for falls and the prediction of frailty. This review reveals the limited use of data science techniques on data from LTC facilities. The low number of articles indicates the need for strategies aimed at the effective utilization of data science techniques and evidence of their practical benefits. There is a need for a wider adoption of these techniques in order to exploit data to their full potential and improve the quality of care in LTC by making data-informed decisions.

DEVELOPMENT OF AN EVIDENCE-BASED FRAMEWORK FOR INNOVATION READINESS OF LONG-TERM CARE ORGANIZATIONS

Abstract Increasing innovation readiness of long-term care organizations is vital to ensure future provision and affordability of care delivery. Against this background, there is a need to obtain more insight into the factors that may lead to innovation readiness of long-term care organizations. Therefore, we conducted a scoping review and an interview study. The scoping review was based on the framework from Arksey and O’Malley and included 44 studies. The interview study was conducted with 16 stakeholders having a professional role in long-term care for older adults in the Netherlands: academics, (top)management, innovation managers and consultants. Based on the results of both studies, we propose a framework of factors contributing to innovation readiness. The framework consists of five main factors enabling innovation readiness of long-term care organizations for older adults: 1) strategic course for innovation 2) innovation journey 3) leadership for innovation 4) learning for innovation and 5) innovative organizational culture. The collective findings support the notion that for innovation readiness the interplay of main factors is vital and benefits from an approach at the individual, team, organizational and inter-organizational levels. Furthermore, our findings indicate that some factors might be more conditional and other factors might play a more supportive role. Research into innovation readiness of health care organizations is a rather new field. Further research directed toward a framework for innovation readiness might deliver a structured approach for managers of long-term care organizations to embed and assess innovation readiness.

Comparing text mining and manual coding methods: Analysing interview data on quality of care in long-term care for older adults.

In long-term care for older adults, large amounts of text are collected relating to the quality of care, such as transcribed interviews. Researchers currently analyze textual data manually to gain insights, which is a time-consuming process. Text mining could provide a solution, as this methodology can be used to analyze large amounts of text automatically. This study aims to compare text mining to manual coding with regard to sentiment analysis and thematic content analysis. Data were collected from interviews with residents (n = 21), family members (n = 20), and care professionals (n = 20). Text mining models were developed and compared to the manual approach. The results of the manual and text mining approaches were evaluated based on three criteria: accuracy, consistency, and expert feedback. Accuracy assessed the similarity between the two approaches, while consistency determined whether each individual approach found the same themes in similar text segments. Expert feedback served as a representation of the perceived correctness of the text mining approach. An accuracy analysis revealed that more than 80% of the text segments were assigned the same themes and sentiment using both text mining and manual approaches. Interviews coded with text mining demonstrated higher consistency compared to those coded manually. Expert feedback identified certain limitations in both the text mining and manual approaches. While these analyses highlighted the current limitations of text mining, they also exposed certain inconsistencies in manual analysis. This information suggests that text mining has the potential to be an effective and efficient tool for analysing large volumes of textual data in the context of long-term care for older adults.

Examining the determinants of health and long-term care for older adults in India through a socio-ecological model - A qualitative assessment.

Ageing is associated with multiple long-term health problems and requires medication management, support with activities of daily living, and attention to psychological needs. This study aimed at exploring the enablers and barriers and psychosocial determinants of long-term care. A qualitative study, using semi-structured interviews and thematic analysis, was carried out from February to June 2018. One-on-one in-depth interviews were carried out with 28 participants, including care recipients (n = 12), caregivers (n = 12), and primary-care physicians (n = 4) at the Community Health Centre in the state of Jammu and Kashmir of India. Attention from family members, leisure activities, faith in the healthcare provider, and a positive attitude towards life were found to be enablers of long-term care. Resource constraints and alienation from the community were barriers. Incorporating the psychosocial needs of care recipients and problems faced by family caregivers is essential in providing good quality care to individuals with chronic illness. The lack of a targeted, nationwide policy has resulted in considerable variability in long-term care services across the country. There is an urgent need to make long-term care an integral part of the Indian health system utilizing a holistic framework to address the health needs of older adults and implementing it through an equitable community-based comprehensive primary health and community-based model.

Embracing Challenges for Population Aging in China: Building Scientific Evidence to Inform Long-Term Care Policymaking and Practice

ABSTRACT In China, rapid aging of the population is driving up demand for healthcare and long-term care services for older adults. This special issue of the Journal of Aging & Social Policy features a collection of studies that provided timely analyses and fresh insights into a wide range of policy relevant topics concerning long-term care for older adults in China. In this introductory article, we orient readers to these studies organized under four themes: migration, caregiving, and elder care challenges; long-term care service users, frontline workers, and workforce challenges; unmet needs across the care span in healthcare, long-term care, and end of life care; and long-term care financing. We highlight major findings and contributions of each study and provide perspectives on key issues within China’s evolving healthcare and social policy contexts. Collectively, these studies contribute to building scientific evidence where it is lacking and supporting evidence-based long-term care policymaking and practice to meet the mounting challenges of population aging in China.

Measuring spatial accessibility to adult day services for older adults using GIS

Adult day services (ADS) provide community-based long-term care for older adults with chronic medical, cognitive, or mental health conditions and/or disabilities that are at risk of needing institutional care. This study aims to (a) measure and compare the spatial accessibility to ADS across major racial and ethnic minority older adult groups, and to (b) identify underserved urban areas which have limited access to ADS. Using Geographic Information Systems (GIS), we found little variation across ethnic and racial groups in accessibility to ADS; instead, most differences are between rural and urban areas. Underserved areas with a high older adult population in Los Angeles County were identified. Future research should incorporate other factors such as the organization’s capability, culture compatibility, types of services, and quality of services which may impact on ADS use among older adults.

Potentially More Out of Reach: Public Reporting Exacerbates Inequities in Home Health Access.

Literature suggests that public reporting of quality may have the unintended consequence of exacerbating disparities in access to high-quality, long-term care for older adults. The objective of this study is to evaluate the impact of the home health five-star ratings on changes in high-quality home health agency use by race, ethnicity, income status, and place-based factors. We use data from the Outcome and Assessment Information Set, Medicare Enrollment Files, Care Compare, and American Community Survey to estimate differential access to high-quality home health agencies between July 2014 and June 2017. To estimate the impact of the home health five-star rating introduction on the use of high-quality home health agencies, we use a longitudinal observational pretest-posttest design. After the introduction of the home health five-star ratings in 2016, we found that adjusted rates of high-quality home health agency use increased for all home health patients, except for Hispanic/Latine and Asian American/Pacific Islander patients. Additionally, we found that the disparity in high-quality home health agency use between low-income and higher-income home health patients was exacerbated after the introduction of the five-star quality ratings. We also observed that patients within predominantly Hispanic/Latine neighborhoods had a significant decrease in their use of high-quality home health agencies, whereas patients in predominantly White and integrated neighborhoods had a significant increase in high-quality home health agency use. Other neighborhoods experience a nonsignificant change in high-quality home health agency use. Policymakers should be aware of the potential unintended consequences for implementing home health public reporting, specifically for Hispanic/Latine, Asian American/Pacific Islander, and low-income home health patients, as well as patients residing in predominantly Hispanic/Latine neighborhoods. Targeted interventions should focus on raising awareness around the five-star ratings.

Long-Term Care for Older Adults in Rural China: Findings from the Recent Chinese Longitudinal Healthy Longevity Survey

Population aging poses a significant challenge for rural China’s long-term care system. Based on a nationally representative elderly survey in 2018, this paper attempts to provide a recent profile of the demand for and supply of long-term care among the rural elderly.

Implementation of long-term care for older adults: a literature review

To explore the abuse and neglect phenomenon in long-term care facilities for the elderly population. The systematic review comprised search on PubMed, CINAHL, MEDLINE and ScienceDirect databases following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The key words used were older people care, long-term care, older people and older adults. Articles published in the last 5 years between 2017 to 2021 in recognised English-language journals and whose full text was available on the websites were included. Details of the selected studies were noted and analysed. Of the 336 studies initially identified, 15(4.46%) were reviewed in detail. Of them, 3(20%) had been done in North America, 6(40%) in Europe and 6(40%) in Asia. The prevalence of abuse and neglect in long-term care facilities for the elderly was generally high, with nursing home staff mostly involved due to burnout syndrome or related to personal factors, such as childhood adversity and work-related stress. Better understanding is critical for improving the quality of care in long-term care facilities to prevent abuse and neglect with the elderly.

Creating a safe and inclusive living environment for LGBTQ+ older adults: A review of research, policy and literature

The proportion of older adults (65+) is increasing rapidly. Therefore, caring for older adults must be a priority of our society, in order for them to have a dignified life in old age. LGBTQ+ older adults are a heterogeneous, growing subset of the ageing population, with many challenges and needs. Old age is a vulnerable period for all populations, and LGBTQ+ older adults are even more vulnerable in this period. They face discrimination and stress in finding a safe and inclusive environment. Our research used the descriptive method - a scientific literature review. The selection of articles was made according to the following inclusion criteria: accessibility, scientificity, content relevance, and topicality. The issue of LGBTQ+ older adults in search of a suitable living environment through a qualitative content analysis of eight articles was addressed. We find that it is important for this population group to feel accepted, safe and connected to the community in their living environment. Studies still point to discrimination and stigma being experienced by LGBTQ+ people, especially in the institutional environment, which affects physical and mental health. In the future, we must ensure that employees involved in the long-term care of older adults have sufficient knowledge about this vulnerable group of the population. A safe and inclusive environment for LGBTQ+ older adults must be promoted, where they can be accepted and feel connected to the community during their ageing.

The development of an automatic speech recognition model using interview data from long-term care for older adults

In long-term care (LTC) for older adults, interviews are used to collect client perspectives that are often recorded and transcribed verbatim, which is a time-consuming, tedious task. Automatic speech recognition (ASR) could provide a solution; however, current ASR systems are not effective for certain demographic groups. This study aims to show how data from specific groups, such as older adults or people with accents, can be used to develop an effective ASR. An initial ASR model was developed using the Mozilla Common Voice dataset. Audio and transcript data (34 h) from interviews with residents, family, and care professionals on quality of care were used. Interview data were continuously processed to reduce the word error rate (WER). Due to background noise and mispronunciations, an initial ASR model had a WER of 48.3% on interview data. After finetuning using interview data, the average WER was reduced to 24.3%. When tested on speech data from the interviews, a median WER of 22.1% was achieved, with residents displaying the highest WER (22.7%). The resulting ASR model was at least 6 times faster than manual transcription. The current method decreased the WER substantially, verifying its efficacy. Moreover, using local transcription of audio can be beneficial to the privacy of participants. The current study shows that interview data from LTC for older adults can be effectively used to improve an ASR model. While the model output does still contain some errors, researchers reported that it saved much time during transcription.

THE IMPACT OF PUBLIC REPORTING ON INEQUITIES IN HIGH-QUALITY HOME HEALTH UTILIZATION

Abstract Literature suggests that public reporting of quality may exacerbate disparities in access to high-quality post-acute and long-term care for older adults. The objective of this study was to evaluate the impact of the home health (HH) 5-star ratings on changes in high-quality HH agency (HHA) use, by race, ethnicity, income level, and place-based factors. Using a difference-in-differences framework, we found that after introduction of 5-star ratings adjusted rates of high-quality HHA use increased for all HH users, except for Latinx, Asian American/Pacific Islander, and low-income HH users, and disparities in access to high-quality HHAs were exacerbated for each of these groups. Last, we found that users within predominantly Latinx and lower income neighborhoods had a significantly greater decrease in their use of high-quality HHAs. Policymakers should be aware of the potential unintended consequences of public reporting and should consider adding measures of equity to the publicly reported information.

IMPLICATION OF SLEEPING DURATION ON THE OVERALL HEALTH STATE OF FAMILY CAREGIVERS

Abstract Family caregiving is integral to long-term care for older adults. The National Alliance for Caregiving and AARP Public Policy Institute in 2020 estimated there are 53 million Americans providing care for loved ones. Caregiving intensity often impacts the health and well-being of caregivers, including influencing their sleep duration. The Centers for Disease Control and Prevention (CDC) recommended an average of 7 or more sleeping hours for adults. Unfortunately, most caregivers' average hours of sleep per night do not meet this recommendation. Using secondary data from the Long-Term Caregiving study conducted by the AP-NORC Center for Public Affairs Research, the present study establishes the effect of sleep duration on the overall health of family caregivers while adjusting for socio-demographic factors. A binary logistic analysis was conducted to infer the effects of sleep duration, age, gender education, race, employment status, and household income relative to reporting high or low overall health wellbeing. The binary logistic model was statistically significant, χ2(16) = 77.5, p < .0001, Nagelkerke R2=0.111. Family caregivers with a sleeping duration of 7 hours or more were twice as likely to report a high overall health status than those who slept less than 7 hours (OR=2.08, 95%CI [1.52 – 2.84]). Increasing household income and being employed were positively associated with high overall health status. We do not find a statistically significant effect of gender, education level, age group, and race on overall health status. We recommend that policies that support family caregivers at a micro and macro level be implemented.