Publications

Abstract

Standardized nursing languages help nurses articulate issues with patients, forming the groundwork for the selection of nursing interventions aimed at achieving outcomes. However, the application of standardized nursing linkages on nursing processes and nursing home residents' outcomes, and the identification of facility and resident factors, remains unexplored. The purpose of this study was to examine nursing home facility and resident factors on the use of NANDA, Nursing Interventions Classification, and Nursing Outcomes Classification (NNN) and frequently occurring NNN linkages surrounding nursing home residents in Korea. Data were collected from 53 nurses of 273 residents in 19 nursing homes in Korea using a newly developed smartphone application. Descriptive statistics, analysis of variance, and analysis of covariance were used for the data analysis. Nine NNN linkages were identified in this study, mostly centered on fall prevention. We also identified that residents' factors (including acuity, age, and admission period) and organizational factors (including year of facility establishment and facility ownership status), were related to the use of NNN. Nursing home nurses' clinical and critical judgment and the utilization of standardized nursing languages to select proper nursing interventions and outcomes should be based on both resident and organizational factors.

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Standardized nursing languages play a crucial role in facilitating holistic nursing care, but their full benefits are still being realized. However, the implementation of NNN linkages and their impact on nursing processes and nursing home (NH) residents' outcomes, as well as the identification of facility and resident factors, remain unexplored. The purpose of this study was to examine (a) frequently occurring NNN linkages and (b) related facility and resident factors on the use of NANDA, NIC, and NOC for NH residents. Data were collected from 273 residents in 19 NHs in Korea 19 NHs in Korea, with structured questionnaires administered to NH registered nurses using a newly developed smartphone application. Descriptive statistics, ANOVA, and ANCOVA were used for the data analysis. We identified that factors, such as case mix index (resident acuity), age of resident, year of facility establishment, facility ownership status, and admission period of residents, are related to the use of NANDA, NIC, and NOC. Nine NNN linkages were newly identified in this study, mostly centered on fall prevention. The findings provide various approaches to address the risk of falls in NHs considering environmental, physical, and psychological factors. This study contributes to the foundation of evidence related to NHs by collecting specific data on the nursing process for the physiological, behavioral, safety, family, health system, and community areas applied to the residents of long-term care facilities at the national level. Future research in different settings and tailored population is needed.

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With the increase of prevalence in autism, the need for efficient public health support has been amplified. Socially-assistive robots (SARs) have been found effective in engaging and interacting with autistic children, however, the perception intelligence during interaction still needs more domain-specific knowledge in terms of understanding children's behaviors. The Family Observation Schedule-Second Version (FOS-II) is one of the key methods in assessing parent-child interactions in developmental disabilities, yet its manual annotation demands considerable time and effort. This study proposes a multimodal artificial intelligence (AI) model using video and audio inputs for automated FOS-II annotation. Utilizing advanced deep learning for behavior recognition, this method offers rapid, cost-effective FOS-II scaling. It will thus enhance the capability of socially assistive robots to understand human behaviors and support the advancement of digital health research for children with autism. The visual perception in home settings are most likely based on uncontrolled environments, so it is crucial to develop algorithms that can robustly work with video-log data with uncontrolled quality. Ultimately, it aims to ease the burden on parents and caregivers, streamlining the monitoring and treatment of challenging behaviors in autism.

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This thesis presents a hybrid methodology combining finite element analysis (FEA) with machine learning to enhance defect detection and lifecycle assessment for composite aerospace elements.

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