bluuebunny/crossref_metadata_2025_split huggingface dataset:
PROMOTING MENTAL HEALTH LITERACY AMONG OLDER ADULTS IN THE EAST: AN INTEGRATIVE FRAMEWORK
Authors: Yau Jessie Ho-Yin , Kanagawa Hotinpo Sky , Lo Maggie Wai-Shan , Wong Stella Lai-Kuen , Wong Gloria Hoi-Yan , Lum Terry , Liu Tianyin 
Published: December 2023

Abstract
Although mental health literacy is of considerable importance to advance the welfare of older adults, previous research has shown that the public has a poor understanding of mental health and recognition of mental health symptoms. Traditional mental health literacy promotion programmes utilized a top-down approach; no bottom-up model had been used. Moreover, the existing participatory research paradigms were developed in the West and might not be applicable in the East, given different socioeconomic and cultural contexts. This research aimed to fill the gap by co-creating an integrative framework with stakeholders to promote mental health literacy among older adults in the East using a community-based participatory approach as a case illustration. We conducted a mental health literacy promotion pilot project in five Hong Kong districts from May 2021 to August 2023, spanning over 2 years. Each district formed an action committee, which comprised 5-10 community older adults and 2 social workers, and 2 researchers. Each district had its own promotional activities initiated and designed by older adults, including street booths, art workshops, and publicity materials to promote mental health and introduce relevant information and resources. We conducted six focus groups (n=25) with stakeholders, and we recorded our experiences on reflection notes. Collected data suggested specific elements are important for an integrative framework in the East, including participant empowerment, stakeholders’ role and expectation management, and potential community contribution. We advance gerontological research and practice by providing an integrative framework to promote mental health literacy in the East. Implications will be discussed further.
ADMIRATION, SKEPTICISM, INDECISIVENESS? ATTITUDES TOWARD AI IN CHINESE MIDDLE-AGED AND OLDER ADULTS
Authors: Lau Bobo Hi-po , Shum Eric Ngai Yin , Lui Fan Yin-Fong , Lam Gigi , Kwok Alex Pak-Ki , Tsang Chung-Kin 
Published: December 2023

Abstract
In recent years, AI is increasingly visible in daily lives from chatbots in banking apps and SIRI, and generative AI such as ChatGPT, to more controversial usage concerning facial recognition, online surveillance, and even self-driving cars. Attitudes toward AI may predict acceptance of AI-facilitated applications and the Web 3.0 at-large. In June 2023, we conducted a survey with 750 smartphone users aged 45 years or older (Max = 78) in Hong Kong as a part of a wider effort to elucidate the digital divide post-pandemic. Using principal component analysis with varimax rotation, we found three components from the General Attitude to Artificial Intelligence Scale - Negative, Positive, and Powerful (compared to humans). Subsequently, with latent profile analysis we revealed three profiles: (i) Admiration (18.4%; high on positive and powerful but low on negative); (ii) Skepticism (12.3%; high on negative but low on positive and powerful), and (iii) Indecisiveness (69.3%; moderate on all three components). Admiration was more likely to be male, with higher income, better self-rated health, and greater mobile device proficiency, optimism, innovativeness, but also insecurity with technology, compared to Indecisiveness, and then Skepticism. Corroborating these quantitative findings, AI, or generative AI in specific, were only sparsely featured in a concurrent focus group study on smartphone usage with a similar cohort of smartphone users. Those who mentioned were appreciative of their superior power to provide information beyond traditional search engines; yet apprehension toward associated fraud and deception was also highlighted.
REDUCING BENEVOLENT AGEISM AMONG UNDERGRADUATE HEALTHCARE MAJOR STUDENTS IN A GERONTOLOGY CLASS
Authors: Cao Jiawei , Cui Jialin 
Published: December 2023

Abstract
While gerontologists have devoted continuing efforts to combating ageism through education and interventions, benevolent ageism remains to be a more subtle and covert concept to understand and recognize. This study aims to examine the effectiveness of an innovative pedagogy to reduce benevolent ageism among undergraduates majoring in healthcare with an aging focus. Two classes (36 students in the intervention group and 43 in the control group) that were both enrolled in an entry-level gerontology class were recruited. The intervention consisted of a semester-long synchronized lecture with pre-lecture quizzes, videos, examples, and in-class group projects regarding benevolent ageism, while the control group received a regular gerontology class with no additional emphasis on ageism. At the beginning and the end of the semester, all participants completed a questionnaire that was built upon the translated Ambivalent Ageism Scale (AAS) benevolent subscale. Mixed-design ANOVA was conducted to examine the effects of the pedagogy (intervention vs. control) and time (pre-test and post-test) on students’ benevolent score. The score for both groups did not differ at pre-test, and the results showed a significant interaction of group by time (F (1, 77) =13.24, p &lt; .001). The benevolent subscale score of the intervention group decreased significantly over time, while the control group only showed a slight but insignificant decrease in the score. Findings from our study suggested that this innovative pedagogy may be useful in reducing benevolent ageism among healthcare major undergraduates. However, further randomized studies are needed to confirm our findings.
REFRAMING AGING FOR HIGHER EDUCATION IN HEALTH CARE PROFESSIONS
Authors: Coyle Caitlyn , Bui Cindy , Massihzadegan Setarreh , Ballout Suha , Mutchler Jan 
Published: December 2023

Abstract
As students in health care professions will serve older patients, it is critical to proactively reframe aging in their education, confront implicit biases, and encourage age-inclusivity before they enter the workforce. We developed a training to address the limited focus on aging and later life among undergraduate curricula in health care professional disciplines (e.g., nursing, health sciences). The 1-hour, online training was piloted among current undergraduate nursing students (N=69). The content uses data and real-person interviews to introduce demographic trends in aging, define and deconstruct ageism concepts, illustrate the impact of ageism on individual health and health care, and highlight the diverse experiences of aging. The training also includes communication tools and interactive activities, such as reflection questions and case studies, to engage students in their own aging experience and to garner empathy for others’ aging experiences. Pre- and post-training evaluations revealed that the training impacted students in expressing positive behavioral change towards age-inclusivity and awareness at internal, interpersonal and institutional levels. Evaluation results also showed statically significant evidence that the training increased students’ knowledge about facts versus stereotypes about aging, confidence in understanding ageism and its relevance to health care, and consideration of working with older adults in their career. This pilot research project encourages other higher education institutions to incorporate training and education in their curricula that distinctively focuses on later life and reframing aging in a long-term effort towards improving health care for older people and the experiences of health care professionals who serve them.
INNOVATING HOME HEALTH DELIVERY TO IMPROVE VETERANS’ ACCESS TO CARE AND CARE COORDINATION
Authors: Benzel Heather , Carter Jammie , Censky Dina , Janssen Daniel , Murphy Rachel , Putz Leann , Davila Heather , Roman Irene San 
Published: December 2023

Abstract
Nearly 350,000 Veterans receive home health services through the Veterans Health Administration (VA) annually. Historically, VA has purchased most home health services from contract agencies in the community. VA leaders have expressed growing concerns about Veterans’ access to home health, especially in rural areas. Coordinating care between VA and contract agencies is an ongoing challenge. To address these challenges, the VA Midwest Health Care Network (VISN 23) developed the VA Home Health (VAHH) pilot program. The program launched at the Iowa City VA and is currently operating at 4 sites in Iowa and South Dakota. Through VAHH, local VA Medical Centers hire home health staff directly (nurses, aides, physical and occupational therapists), rather than purchase these services through contract agencies. In this presentation, we describe program development, challenges, and early evaluation findings. In fiscal year (FY) 2022, 200 Veterans received VAHH. So far in FY2023, over 700 Veterans have been served. Pre/post comparisons involving Veterans receiving VAHH show modest reductions in emergency room visits (727 vs. 664 visits) and inpatient admissions (227 vs. 192 admissions). Overall hospital lengths of stay have reduced more dramatically (2132 vs. 1390 total days). VAHH staff note close communication with Veterans’ primary care teams, and Veterans describe being highly satisfied with the program. Interest in VAHH is growing nationally, particularly as access to home health in many regions is limited. Early lessons from the VAHH pilot program could inform VA decisions about home health delivery and assist other sites in improving care for Veterans.
THE PREDICTIVE EFFECT OF DIFFERENT MACHINE LEARNING ALGORITHMS FOR PRESSURE INJURIES: A NETWORK META-ANALYSES
Authors: Qu Chaoran , Yang Xiufen , Peng Weisi , Wang Xiujuan , Luo Weixiang 
Published: December 2023

Abstract

Objective
This review aims to systematically synthesize existing evidence to determine the effectiveness of applying machine learning algorithms for pressure injury management, to further evaluate and compare pressure injury prediction models constructed by numerous machine learning algorithms, and to derive evidence for the best algorithms for predicting and managing pressure injuries.

Methods
A systematic electronic search was conducted in the EBSCO, Embase, PubMed, and Web of Science databases. We included all retrospective diagnostic accuracy trials and prospective diagnostic accuracy trials constructing a predictive model by machine learning for pressure injuries up to December 2021. The network meta-analysis was conducted using statistical software R and STATA. The certainty of the evidence was rated using the QUADAS-2 tool.

Result
Twenty-five clinical diagnostic trials with a total of 237397 participants were identified in this review. The results of our study revealed that pressure injury machine learning models can effectively predict these injuries. Combining the algorithms separately yields the main

results
decision trees (sensitivity: 0.66, specificity: 0.90, AUC: 0.88), logistic regression (sensitivity: 0.71, specificity: 0.83, AUC: 0.84), neural networks (sensitivity: 0.73, specificity: 0.78, AUC: 0.82), random forests (sensitivity: 0.72, specificity: 0.96, AUC: 0.95), support vector machines (sensitivity: 0.81, specificity: 0.81, AUC: 0.88). According to the analysis of ROC and AUC values, random forest is the best algorithm for the prediction model of pressure injury.

Conclusions
This review revealed that machine learning algorithms are generally effective in predicting pressure injuries, the random forest algorithm is the best algorithm for pressure injury prediction.
FACTORS AFFECTING THE ACCEPTANCE OF CARE ROBOTS BY OLDER ADULTS: FOCUSING ON TECHNICAL FACTORS
Authors: Yoon Hee Jeong , Lee Hyun Ju , Ban Si Woo , Kim Ha Yul , Cha Eun Gyo , Lee Jung Wan , Shin Hye Ri , Kim Young Sun 
Published: December 2023

Abstract
Care robots have the potential to support the healthy and independent lives of older adults and alleviate the burden on care workers. However, the potential benefits of care robots can only be realized if they are accepted. Given that most older adults do not have experience with care robots and lack digital literacy, an understanding of the factors affecting the acceptance of care robots is important. The goal of this study was to analyze the factors that affect older adults’ acceptance of care robots by distinguishing the factors into sociodemographic, health, and technical factors. Data were collected from older adults aged 60 or over living in the community of Korea in 2022 (N= 506, mean age=70.11±7.74, women=54.55%). Hierarchical regression analysis was conducted to identify the factors related to acceptance of care robots. The results showed that factors influencing older adults’ acceptance of care robots were significant in sociodemographic factors (education and place of residence), health factors (self-reported health and instrumental activities for daily living), and technical factors (digital literacy and technostress). The findings indicate that interventions are needed to increase the acceptance of care robots by vulnerable groups with low levels of education, living in rural areas, and poor health conditions, and that it is also important to increase their digital literacy and lower their technostress. This study would provide valuable guidelines to stakeholders such as service providers, and policymakers to increase the acceptance of care robots and improve the quality of life of older adults.
IMPACT OF THE TEACHING NURSING HOME ON NURSING STUDENT’S COMPETENCIES AND WILLINGNESS TO WORK IN LONG-TERM CARE
Authors: Degenholtz Howard , Dunbar-Jacob Jacqueline , Ratliff Kim , Fick Donna , Dickson Chelsea , Kitt-Lewis Erin , Franke Emily , Campbell Kenna 
Published: December 2023

Abstract
The Teaching Nursing Home (TNH) is designed to improve the long-term care workforce and improve quality of care in nursing homes. The initiative has multiple components: enhanced clinical rotations for nursing students with partner schools of nursing, implementation of the Institute for Healthcare Improvement Age-Friendly Health System “4M” quality improvement model, and an online learning network. Nursing students at three schools of nursing participated in clinical rotations at four regional nursing homes. The experience was limited to students in one specific course at each school of nursing. At the beginning and end of the Spring 2023 semester, students rated their competence in: patient assessment, collaborating with the care team, gathering clinical information, medication review, eliciting resident values, and health promotion. Students also rated their preferences for working in long-term care and with older adults. Data from 165 responses at the start of semester (January 2023) and 155 responses to the end of semester (April 2023) survey were analyzed. Analysis of student responses found that students self-rated competencies improved in all areas. Prior to their clinical experience, students ranked working in long-term care and with older adults lower than other settings or populations. The rankings were unchanged after their clinical experiences. Qualitative analysis found that most student comments (50%) were about skills, while only 1.5% were about potential careers in nursing homes. These findings suggest that the Teaching Nursing Home Program is meeting the pedagogical goals, however attitudinal shifts may require different strategies.
UNDERUTILIZATION OF TRANSITIONAL CARE MANAGEMENT SERVICE AMONG MEDICARE BENEFICIARIES AT HIGH RISK OF READMISSION
Authors: Park Chan Mi , McCarthy Ellen , Shi Sandra , Olivieri-Mui Brianne , Jang Jieun , Kim Dae Hyun 
Published: December 2023

Abstract
The CMS introduced transitional care management (TCM) service in 2013 to improve care transitions from hospital to home and reduce readmission. TCM uptake has been slowly increasing over time; however, it remains uncertain whether TCM is delivered to patients at the highest risk of readmission. We used a 5% random sample of 2015-2019 Medicare fee-for-service claims. TCM service was identified using CPT codes and demographic information, census region, diagnosis codes, and frailty were measured. Of 1,556,347 eligible discharges (mean [standard deviation] age, 78.3 [7.8] years, 56.7% female, 84.2% White), TCM was delivered in 169,536 (10.9%) discharges and 30-day readmission occurred in 123,796 (8.0%) discharges. The 30-day readmission risk was higher among beneficiaries who were &lt; 75 years (8.1%) vs ≥75 years (7.9%), male (8.7%) vs female (7.4%), Black (9.3%) or Hispanic (8.7%) vs White (7.8%) and Asian or Pacific Islander (8.0%), with dementia diagnosis (8.2%) vs without (7.9%), or with greater frailty (robust to severe frailty: 6.1% to 14.1%). TCM use was not necessarily higher among those with high-risk characteristics. Notably, TCM was more likely to be delivered to beneficiaries who were ≥75 years (12.3%) vs &lt; 75 years (8.8%), White (11.3%) and Asian or Pacific Islander (10.8%) vs Black (9.0%) and Hispanic (8.4%), without dementia (11.0%) vs with (10.6%), and pre-frail or mildly frail (11.4%) vs moderately or severely frail (10.0-10.5%). Our study found that TCM is not delivered effectively to Medicare beneficiaries at high risk of readmission, including racial and ethnic minority groups, and those with moderate-to-severe frailty.
PATIENT AGE AND SEX DO NOT APPEAR TO INFLUENCE DECISION MAKING AROUND BIOMARKER TESTING FOR COGNITIVE IMPAIRMENT
Authors: Monane Mark , Johnson Kim , Snider Joy , Turner R Scott , Drake Jonathan , Ortega Julia , Jiang Yan , Braunstein Joel 
Published: December 2023

Abstract
The QUIP I Study (ClinicalTrials.gov Identifier: NCT05477056) was a prospective, single-arm study which included 347 older persons (average age 74, 56% women) presenting with signs and symptoms of cognitive impairment. In a subgroup analysis, we measured the effect of a person’s age and sex on clinical decision making around the PrecivityAD® blood biomarker (BBM) test result. The test result was reported as the Amyloid Probability Score (APS), which measures the likelihood of a positive result on an amyloid PET scan. Clinical decision making was recorded by clinician survey pre- and post-BBM testing. Clinician-reported probability of Alzheimer’s disease (“AD”) changed pre-test to post-test from 58% to 23% (Low APS group) and from 71% to 89% (High APS group) (p &lt; 0.0001 for all APS groups). The relationships between APS and change in diagnostic certainty were not significantly different as analyzed by age (p=0.344 for Low APS, p=0.292 for High APS) or sex (p=0.167 for Low APS, p=0.213 for High APS). Overall use of AD drug therapy decreased from 48% to 26% (Low APS group) and increased from 56% to 88% (High APS group) (p &lt; 0.0001 for all APS groups). The relationships between APS and change in medication prescribing were not significantly different as analyzed by age (p=0.4534 for Low APS, p=0.9939 for High APS) or sex (p=1 for Low APS, p=0.931 for High APS). We believe that the current study results help to underscore the usefulness and generalizability of this BBM among older adults in clinical care pathways.
UNRAVELING COMORBIDITY PREVALENCE AND PM2.5 EXPOSURE AMONG MIDDLE-AGED AND ELDERLY: A SPATIAL-TEMPORAL ANALYSIS
Authors: Wei Linjiang , Zhang Liangwen , Fang Ya 
Published: December 2023

Abstract

Objective
This study describes regional differences and dynamic changes in the prevalence of comorbidities among middle-aged and elderly people with chronic diseases (PCMC) in China from 2011-2018, and explores distribution patterns and the relationship between PM2.5 and PCMC, aiming to provide data support for regional prevention and control measures for chronic disease comorbidities in China.

Methods
This study utilized CHARLS follow-up data for ≥45-year-old individuals from 2011, 2013, 2015, and 2018 as research subjects. Missing values were filled using the random forest machine learning method. PCMC spatial clustering investigated using spatial autocorrelation methods. The relationship between macro factors and PCMC was examined using Geographically and Temporally Weighted Regression, Ordinary Linear Regression, and Geographically Weighted Regression.

Results
PCMC in China showing a decreasing trend. Hotspots of PCMC appeared mainly in western and northern provinces, while cold spots were in southeastern coastal provinces. PM2.5 content was a risk factor for PCMC, the range of influence expanded from the southeastern coastal areas to inland areas, and the magnitude of influence decreased from the southeastern coastal areas to inland areas.

Conclusion
PM2.5 content, as a risk factor, should be given special attention, taking into account regional factors. In the future, policy-makers should develop stricter air pollution control policies based on different regional economic, demographic, and geographic factors, while promoting public education, increasing public transportation, and urban green coverage.
RELATIVE AND ABSOLUTE METABOLIC COST OF DAILY PHYSICAL ACTIVITIES ACROSS THE LIFESPAN
Authors: Manini Todd , Handberg Eileen , Mardini Mamoun , Sandesara Bhanu , Casanova Ramon , Corbett Duane , Wanigatunga Amal 
Published: December 2023

Abstract
Americans spend over 90% of their activity related energy expenditure performing common daily activities. However, there is a gap in knowledge pertaining to potential age-related differences in the metabolic cost of performing these activities which diminishes proper physical activity prescription. We hypothesized age differences in both the absolute and relative metabolic cost of performing common daily physical. Participants (n=248, age: 20-94 yrs) performed 33 activities (e.g. dressing, walking, making a bed, gardening, computer work) in a standardized setting. A portable indirect calorimeter was worn for approximately 6-10 minutes to achieve steady-state metabolic rate. A graded exercise treadmill test was performed to quantify peak metabolic rate. A metabolic equivalent (MET as a function of 3.5 milliliter•min-1•kg-1) and percentage of peak were used to express absolute and relative metabolic rate, respectively. Watching television (1.0+/-0.36 METs; 15.6+/-5.7% of peak) produced the lowest and climbing stairs (6.2+/-2.2 METs; 84.8+/-17.0% of peak) elicited the highest metabolic rate. Results from mixed effect regression models adjusting for health conditions, demographics and anthropometry showed no association between absolute MET and age. However, when expressed relative to peak, adjusted models showed a strong association with age (0.66+/-0.33% per year, p=0.006). As an example, adults &lt; 40 yrs old worked at 34.1+/-14.8% of peak during a leisurely walk while those 80+ years old worked at 64.2+/-16.3%. Aging is associated with a substantial increase in the relative metabolic cost of performing common daily physical activities. These results could have implications on prescribing physical activity older adults to achieve higher fitness.