Background: In recent years, increased attention has been given to using deep learning (DL) of artificial intelligence (AI) in healthcare to address nursing challenges. The adoption of new technologies in nursing needs to be improved, and AI in nursing is still in its early stages. However, the current literature needs more clarity, which affects clinical practice, research, and theory development. This study aimed to clarify the meaning of deep learning and identify the defining attributes of artificial intelligence within nursing. Methods: We conducted a concept analysis of the deep learning of AI in nursing care using Walker and Avant’s 8-step approach. Our search strategy employed Boolean techniques and MeSH terms across databases, including BMC, CINAHL, ClinicalKey for Nursing, Embase, Ovid, Scopus, SpringerLink and Spinger Nature, ProQuest, PubMed, and Web of Science. By focusing on relevant keywords in titles and abstracts from articles published between 2018 and 2024, we initially found 571 sources. Results: Thirty-seven articles that met the inclusion criteria were analyzed in this study. The attributes of evidence included four themes: focus and immersion, coding and understanding, arranging layers and algorithms, and implementing within the process of use cases to modify recommendations. Antecedents, unclear systems and communication, insufficient data management knowledge and support, and compound challenges can lead to suffering and risky caregiving tasks. Applying deep learning techniques enables nurses to simulate scenarios, predict outcomes, and plan care more precisely. Embracing deep learning equipment allows nurses to make better decisions. It empowers them with enhanced knowledge while ensuring adequate support and resources essential for caregiver and patient well-being. Access to necessary equipment is vital for high-quality home healthcare. Conclusion: This study provides a clearer understanding of the use of deep learning in nursing and its implications for nursing practice. Future research should focus on exploring the impact of deep learning on healthcare operations management through quantitative and qualitative studies. Additionally, developing a framework to guide the integration of deep learning into nursing practice is recommended to facilitate its adoption and implementation.

Introduction: Owing to the increased prevalence of type 2 diabetes mellitus (T2DM) and the high proportion of patients with uncontrolled T2DM, effective interventions for disease management are needed. Objective: The study aim was to test the effects of brief motivational interviewing (MI) on patients’ self-management, medication adherence, and glycemic control. Methods: A single-group repeated measures trial was used to examine the effects of usual care only and usual care plus telephone-based brief MI. Participants were 29 patients with uncontrolled T2DM recruited from a rural primary care setting in Nakhon Sawan, Thailand. Participants received usual care during the first 4 weeks, followed by usual care plus brief MI during weeks 4– 8. Outcomes of self-management, medication adherence, fasting blood sugar (FBS) levels, and hemoglobin A1c (HbA1c) levels were assessed at baseline, 4 weeks, and 8 weeks. Data were analyzed using descriptive statistics, one-way repeated measures analysis of variance, and Friedman test. Results: Significant changes in self-management (p < 0.001), medication adherence (p < 0.001), and FBS (p < 0.05) were observed over the 8-week study. In multiple comparisons, self-management was the only parameter significantly different across baseline, 4, and 8 weeks (p < 0.05, < 0.001, and < 0.001, respectively); medication adherence was significantly different between 4 and 8 weeks (p < 0.05), and between baseline and 8 weeks (p < 0.001); and FBS was significantly different between 4 and 8 weeks (p < 0.05). HbA1c declined over the 8-week study, but not significantly. Conclusion: An intervention combining telephone-based brief MI with usual care significantly increased self-management, medication adherence, and glycemic control (ie, FBS) after 4 weeks, whereas usual care only significantly increased self-management. Phone-based brief MI may be an effective way for healthcare providers to remotely enhance patients’ self-management and glycemic control, thus reducing barriers related to time and geographic location.

Introduction: Dependent older persons who are limited in their daily activities affect physical, mental, social ability needs help in the family and the community caregiving. Objective: The study aimed to analyze the relationship between caregivers’ characteristics and health conditions, social support, the burden of care, and the quality of respect for the dependent older persons. Methods: The descriptive study design applies in using a Donabedian framework, including structure, process, and outcome with House’s social support. The sample consists of 102 family caregivers currently caring older people with dependency in Bangkok, Thailand. Data were collected through questionnaires, semi-structured interviews, and care quality assessments through tested content validity from 3 health experts, which received a score of 0.97. Descriptive statistics and correlation coefficients were used to analyze the information and tested using Spearman’s rank correlation; acceptable criteria were obtained before collecting the actual data. Results: The results showed that they were statistically significantly related to social support and quality of care. When considering each aspect, social support was associated with the quality of physical and psychological care. Resource social support was to relate the quality of medical and financial aspects. Social support of acceptance and appreciation is associated with quality, environmental, physical, psychological, human rights, and economic elements; information social support was significantly related to information supervision quality. Discussion: This study found that personal factors in age and comorbidity affect the burden of care in family caregivers. In addition, good social support will affect the quality of respect for the dependent older persons in psychological, medical, human rights, and financial dimensions. In particular, the appraisal support, incredible care, attentiveness, and dedication to care will result in a good quality of care in all areas except the quality of physical dimensions.

Background: The coronavirus disease 2019 (COVID-19) pandemic has significantly impacted family caregivers of older dependents, leading to fatigue, anxiety, depression, and financial burdens, which in turn diminish their quality of life. This study investigated the integration of community participatory systems in older care to address potential COVID-19 recurrence and develop policy proposals for the integrated care of dependent older people. Methods: An explanatory sequential design was used, with stratified random sampling across six provinces, resulting in a sample of 440 family caregiver dyads and 154 full-time community stakeholders (October 2021--May 2022). Five validated tools were employed, with a content validity of 0.91 and a Cronbach’s alpha of 0.69. The data analysis included descriptive statistics and thematic analysis. Results: Among the 440 primary caregiver households, 42.73% of the older people had moderate dependence, 30.00% had severe dependence, and 27.27% had complete dependence, with common health issues, including chronic illness and comorbidities. Most older adults lived with their children/grandchildren, had insufficient income, and were informed about COVID-19 prevention measures. Family caregivers, predominantly female (average age 54), had multiple underlying conditions and provided an average of 37 hours of care per week, primarily communicating via phones or apps. The 154 stakeholders included public health volunteers and professional nurses, with an average age of 55 years, and were predominantly female (74.55%). They adhered strictly to COVID-19 precautions during their caregiving duties. This study highlights the impact of trust and COVID-19 perceptions on older care, emphasizing the need for specific forms of care, community support, and resources. Perceptions of “trust”, “stay home”, “self-care”, and “distance” significantly influenced care for dependents. The findings will inform health and social workers’ planning, operation, and training, addressing the specific care needs of dependent older adults. Conclusion: This study recommends policy interventions such as budget allocations for essential equipment, better compensation for community work, and the fostering of expertise in elder care. Emphasis is placed on fostering collaborative community agreements.

Dependent older persons with limitations in activities of daily living (ADL) often experience reduced functioning and require ongoing support from family caregivers and community-based care systems. This study aimed to explore the experiences, challenges, and care needs of family caregivers providing care for dependent older persons in urban communities. A qualitative descriptive design was employed. Twenty-five family caregivers residing in Bangkok, Thailand, were purposively recruited. Participants were family members who had provided primary care for a dependent older person for at least six months and were able to communicate in Thai. Data were collected through in-depth, semi-structured interviews guided by Donabedian’s model of healthcare quality. Each interview lasted approximately one hour, and the interview guide was validated by three experts (content validity index = 0.97). Data were analyzed using conventional content analysis. Family caregivers reported six significant challenges: limited caregiving knowledge and skills, time constraints, caregiving isolation, declining personal health, financial burden, and difficulties managing the older person’s emotional and behavioral problems. Eight key needs emerged: access to reliable health information, online health services, transportation assistance, home healthcare, financial and psychosocial support, home environment modification, and recognition from healthcare authorities. Guided by Donabedian’s framework, findings were categorized into inputs (caregiver and system attributes), processes (caregiving activities), and outcomes (quality and continuity of care supported by digital technologies). Family caregivers of dependent older persons in urban settings face complex, interrelated challenges that affect their well-being. Strengthening integrated community and health support systems, together with evidence-based caregiver empowerment programs, is essential for promoting sustainable, high-quality long-term care.