Person-centered care (PCC) is considered the standard to assure quality of care and quality of life in long-term care, benefiting both residents and staff. This study examines the associations between nursing home staff perceptions of person-centered care practices, the organizational system, and work-related attitudes in a sample of 340 nurses and direct care workers across 32 nursing homes in Oregon. Random-intercepts regression models were used to estimate within- and between-nursing home variation in staff perceptions of PCC practices as measured by the Staff Assessment of Person-Directed Care (SA-PDC), and identify characteristics associated with these perceptions. Staff in nursing homes that accept Medicaid reported lower SA-PDC scores, and higher scores were reported in nonprofit nursing homes. Staff perceptions varied extensively within nursing homes, suggesting a lack of staff cohesion regarding core aspects of PCC. Cultivating a supportive work environment is key to promoting person-centered care practices, increasing job satisfaction, elevating affective commitment, and reducing turnover intention.
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http://dx.doi.org/10.1016/j.gerinurse.2021.11.018 | DOI Listing |
Geriatr Nurs
January 2025
Ordine delle Professioni Infermieristiche di Bergamo, via Pietro Rovelli 45, Bergamo 24125, Italy.
Introduction/objective: The relationship between staffing levels and skill mix in nursing homes is poorly documented in Italy. This study aimed to investigate nursing staffing levels and skill mix in Northern Italian nursing homes.
Methods: A cross-sectional observational study was conducted using a questionnaire sent to several nursing homes.
Age Ageing
January 2025
Aging Research Center, Department Neurobiology, Care Sciences and Society, Karolinska Institutet and Stockholm University, Stockholm, Sweden.
Objective: We aimed to investigate the association of sociodemographic, clinical and functional characteristics with the volume of transitions and specific trajectories across living and care settings.
Methods: Using data from the Swedish National Study on Aging and Care in Kungsholmen study, we identified transitions across home (with or without social care), nursing homes, hospitals and postacute care facilities among 3021 adults aged 60+. Poisson and multistate models were used to investigate the association between sociodemographic, clinical and functional characteristics and both the overall volume and hazard ratios (HRs) of specific transitions.
J Med Syst
January 2025
Unitat de Suport a la Recerca Metropolitana Nord, Institut Universitari d'Investigació en Atenció Primària Jordi Gol (IDIAP Jordi Gol), C/ Mare de Déu de Guadalupe, 2, Mataró, 08303, Barcelona, Spain.
Predicting health-related outcomes can help with proactive healthcare planning and resource management. This is especially important on the older population, an age group growing in the coming decades. Considering longitudinal rather than cross-sectional information from primary care electronic health records (EHRs) can contribute to more informed predictions.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Physiological Controls Research Center, University Research and Innovation Center, Obuda University, 1034 Budapest, Hungary.
In light of the demographic shift towards an aging population, there is an increasing prevalence of dementia among the elderly. The negative impact on mental health is preventing individuals from taking proper care of themselves. For individuals requiring hospital care, those receiving home care, or as a precaution for a specific individual, it is advantageous to utilize monitoring equipment to track their biological parameters on an ongoing basis.
View Article and Find Full Text PDFSensors (Basel)
January 2025
German Center for Vertigo and Balance Disorders (DSGZ), LMU University Hospital, LMU Munich, 81377 Munich, Germany.
Instrumented gait analysis is widely used in clinical settings for the early detection of neurological disorders, monitoring disease progression, and evaluating fall risk. However, the gold-standard marker-based 3D motion analysis is limited by high time and personnel demands. Advances in computer vision now enable markerless whole-body tracking with high accuracy.
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