Conectar
This study aimed (1) to describe how trends in pediatric palliative care (PPC) utilization changed from 2002 to 2017, and (2) to examine factors predicting PPC utilization among decedent children in Taiwan.
This retrospective, correlational study retrieved 2002–2017 data from three national claims databases in Taiwan.
Children aged 1 through 18 years who died between January 2002 and December 2017 were included. Pediatric palliative care utilization was defined as PPC enrollment and PPC duration, with enrollment described by frequency (n) and percentage (%) and duration described by mean and standard deviation (SD). Logistic regression was used to examine the associations of various demographic characteristics with PPC enrollment; generalized linear regression was used to examine associations of the demographic characteristics with PPC duration.
Across the 16-year study period, PPC enrollment increased sharply (15.49 times), while PPC duration decreased smoothly (by 29.41%). Cause of death was a continuous predictor of both PPC enrollment and PPC duration. The children less likely to be enrolled in PPC services were those aged 1 to 6 years, boys, living in poverty, living in rural areas, and diagnosed with life-threatening noncancer diseases.
This study used nationwide databases to investigate PPC enrollment and PPC duration among a large sample of deceased children from 2002 to 2017. The findings not only delineate trends and predictors of PPC enrollment and PPC duration but also highlight great progress in PPC as well as the areas still understudied and underserved. This information could help the pediatric healthcare system achieve the core value of family-centered care for children with life-threatening diseases and their families.
Pediatric palliative care should be widely and continuously implemented in routine pediatric clinical practice to enhance quality of life for children and their families at the end of life.
The integration of generative artificial intelligence (AI) into academic research writing has revolutionized the field, offering powerful tools like ChatGPT and Bard to aid researchers in content generation and idea enhancement. We explore the current state of transparency regarding generative AI use in nursing academic research journals, emphasizing the need for explicitly declaring the use of generative AI by authors in the manuscript. Out of 125 nursing studies journals, 37.6% required explicit statements about generative AI use in their authors' guidelines. No significant differences in impact factors or journal categories were found between journals with and without such requirement. A similar evaluation of medicine, general and internal journals showed a lower percentage (14.5%) including the information about generative AI usage. Declaring generative AI tool usage is crucial for maintaining the transparency and credibility in academic writing. Additionally, extending the requirement for AI usage declarations to journal reviewers can enhance the quality of peer review and combat predatory journals in the academic publishing landscape. Our study highlights the need for active participation from nursing researchers in discussions surrounding standardization of generative AI declaration in academic research writing.
To comprehend the current research hotspots and emerging trends in big data research within the global nursing domain.
Bibliometric analysis.
The quality articles for analysis indexed by the science core collection were obtained from the Web of Science database as of February 10, 2023.The descriptive, visual analysis and text mining were realized by CiteSpace and VOSviewer.
The research on big data in the nursing field has experienced steady growth over the past decade. A total of 45 core authors and 17 core journals around the world have contributed to this field. The author's keyword analysis has revealed five distinct clusters of research focus. These encompass machine/deep learning and artificial intelligence, natural language processing, big data analytics and data science, IoT and cloud computing, and the development of prediction models through data mining. Furthermore, a comparative examination was conducted with data spanning from 1980 to 2016, and an extended analysis was performed covering the years from 1980 to 2019. This bibliometric mapping comparison allowed for the identification of prevailing research trends and the pinpointing of potential future research hotspots within the field.
The fusion of data mining and nursing research has steadily advanced and become more refined over time. Technologically, it has expanded from initial natural language processing to encompass machine learning, deep learning, artificial intelligence, and data mining approach that amalgamates multiple technologies. Professionally, it has progressed from addressing patient safety and pressure ulcers to encompassing chronic diseases, critical care, emergency response, community and nursing home settings, and specific diseases (Cardiovascular diseases, diabetes, stroke, etc.). The convergence of IoT, cloud computing, fog computing, and big data processing has opened new avenues for research in geriatric nursing management and community care. However, a global imbalance exists in utilizing big data in nursing research, emphasizing the need to enhance data science literacy among clinical staff worldwide to advance this field.
This study focused on the thematic trends and evolution of research on the big data in nursing research. Moreover, this study may contribute to the understanding of researchers, journals, and countries around the world and generate the possible collaborations of them to promote the development of big data in nursing science.
To investigate the relationship between work readiness and work well-being for newly graduated nurses and the mediating role of emotional labor and psychological capital in this relationship.
A cross-sectional survey was conducted in mainland China. A total of 478 newly graduated nurses completed the Work Readiness Scale, Emotional Labour Scale, Psychological Capital Questionnaire, and Work Well-being Scale. Descriptive statistical methods, Pearson correlation analysis, and a structural equation model were used to analyze the available data.
Newly graduated nurses' work readiness was significantly positively correlated with work well-being (r = 0.21, p < 0.01), deep acting (r = 0.11, p < 0.05), and psychological capital (r = 0.18, p < 0.01). Emotional labor and psychological capital partially mediated the relationship between work readiness and work well-being. Additionally, emotional labor and psychological capital had a chain-mediating effect on the association.
Work readiness not only affects newly graduated nurses' work well-being directly but also indirectly through emotional labor and psychological capital. These results provide theoretical support and guidance for the study and improvement of newly graduated nurses' work well-being and emphasize the importance of intervention measures to improve work readiness and psychological capital and the adoption of deep-acting emotional-labor strategies.
Nurses, assuming a wide range of clinical and patient care responsibilities in a healthcare team, are highly susceptible to direct and indirect exposure to traumatic experiences. However, literature has shown that nurses with certain traits developed a new sense of personal strength in the face of adversity, known as post-traumatic growth (PTG). This review aimed to synthesize the best available evidence to evaluate personal and work-related factors associated with PTG among nurses.
Mixed studies systematic review.
Studies examining factors influencing PTG on certified nurses from all healthcare facilities were included. Published and unpublished studies were identified by searching 12 databases from their inception until 4th February 2023. Two reviewers independently screened, appraised, piloted a data collection form, and extracted relevant data. Meta-summary, meta-synthesis, meta-analysis, as well as subgroup and sensitivity analyses were performed. Integration of results followed result-based convergent design.
A total of 98 studies with 29,706 nurses from 18 countries were included. These included 49 quantitative, 42 qualitative, and seven mixed-methods studies. Forty-six influencing factors were meta-analyzed, whereas nine facilitating factors were meta-summarized. A PTG conceptual map was created. Four constructs emerged from the integration synthesis: (a) personal system, (b) work-related system, (c) event-related factors, and (d) cognitive transformation.
The review findings highlighted areas healthcare organizations could do to facilitate PTG in nurses. Practical implications include developing intervention programs based on PTG facilitators. Further research should examine the trend of PTG and its dynamic response to different nursing factors.
Research on trauma-focused therapies targeting nurses' mental health is lacking. Therefore, findings from this review could inform healthcare organizations on the PTG phenomenon and developing support measures for nurses through healthcare policies and clinical practice.
FreshRSS 