Ewha Womans University · Health Professions
Professor Juh Hyun Shin's research lab specializes in long-term care policy, nursing home quality, and data-driven health outcomes. The lab focuses on the impact of nursing staff composition and staffing levels on resident health, functional status, and quality of life, with an emphasis on using advanced statistical and machine learning methods to analyze large-scale resident assessment data such as the Minimum Data Set (MDS). Key research directions include evaluating the psychometric properties of long-term care assessment tools, applying hierarchical linear modeling and machine learning for predicting resident risks (e.g., falls), and informing evidence-based policy for long-term care insurance systems. The lab integrates health services research with health informatics to improve elder care delivery and outcomes.
Figures are computed from collected data and may differ slightly.
We found consistency in the effects of RN staffing on resident outcomes acceptable. By assessing nurse staffing levels and compositions of nursing staffs, this study contributes to more effective long-term care insurance by reflecting on appropriate policies, and ultimately contributes to the stable settlement of the long-term care insurance system for elders.
The purpose of this article is to review the advantages and disadvantages of using Minimum Data Set (MDS) data for nursing research, the psychometric characteristics of the MDS 2.0, and threats to the validity of its psychometric characteristics. The defined major advantages of the MDS are: (a) it provides continuous evaluation of residents' health and functional status, and (b) it enables facility evaluation at the nursing home level. The reviewed articles from the literature report that MDS 2.
Hierarchical linear model is a powerful statistical method that can be applied to longitudinal research to evaluate an intervention at multiple levels. The major differences between the repeated-measures ANOVA and the HLM can be summarized as follows: The HLM (a) has less strict assumptions, (b) has more flexible data requirements (dealing with the missing data), and (c) stresses individual change over group differences. More stringent assumptions should be satisfied in repeated-measures ANOVA t
<i>Background:</i> A machine learning (ML) system is able to construct algorithms to continue improving predictions and generate automated knowledge through data-driven predictors or decisions. Objective: The purpose of this study was to compare six ML methods (random forest (RF), logistics regression, linear support vector machine (SVM), polynomial SVM, radial SVM, and sigmoid SVM) of predicting falls in nursing homes (NHs). <i>Methods:</i> We applied three representative six-ML algorithms to t
The purpose of this study was to complete an integrated literature review of the relationship between staffing and quality outcomes in nursing homes. The majority of the reviewed studies showed better outcomes with higher nursing staff but depended heavily on cross-sectional observational studies and failed to differentiate RNs from other nursing staff. A total of 28 articles relating nurse staffing and quality outcomes were systematically reviewed and synthesized. However, each study examined d
This is a preliminary study to investigate the relationship between nursing staffing and QOL for nursing home residents. Further examination is needed to confirm the relationship and provide policy guidelines, including nurse staffing recommendations.
The use of dolls as a therapeutic intervention for nursing home residents with dementia is relatively new. The current article describes a research study implemented with nursing home residents in Korea to examine the effects of doll therapy on their mood, behavior, and social interactions. A one-group, pretest-posttest design was used to measure the impact of doll therapy on 51 residents with dementia. Linear regression demonstrated statistically significant differences in aggression, obsessive
Turnover of nursing home staff and length of tenure may contribute to the more effective management of nursing homes, higher-quality long-term care insurance, and RN-staffing-related laws. Assessing staff characteristics and the tenure of employees promotes the effective management of nursing homes.
The random forest model showed the greatest accuracy for predicting PUs in nursing homes (NHs). Diverse factors that predict PUs in NHs including NH characteristics and residents' characteristics were identified according to diverse ML methods. These factors should be considered to decrease PUs in NH residents.
Korea requires a strong regulatory apparatus for nurse staffing in health-care organizations to improve the quality of its health-care services and patient safety.
This study investigated the relationship between nurse staffing and quality of life (QOL) in Western New York State nursing homes. This was a cross-sectional, correlational study. The independent variables were hours per resident day (HPRD), skill mix, and turnover rate of nursing staff. The outcomes were measured using the self-reported QOL instrument. No coefficients were statistically significant with registered nurses' (RNs) HPRD. Certified nursing assistant (CNA) HPRD had a statistically si
The purpose of this study was to investigate the impact of nurse staffing, skill mix, and stability on resident health outcomes in nursing homes. Methods: This study used a cross-sectional design with proportionate stratified sampling. A total of 53 nursing homes of all 3,261 nursing homes in Korea participated in this study. The number of residents per nursing staff, hours per resident day (HPRD), skill mix, and turnover rate of each nursing staff were used as independent variables. Residents'
This study supported about the contributions of increased input of Registered Nurses, additional to previous longitudinal studies. The nursing homes in Korea should have mandatory Registered Nurse placement for optimal quality of care.
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