Problem-Solving Skills Training for Parents of Children With Chronic Health Conditions: A Systematic Review and Meta-Analysis.

Importance: Problem-solving skills training (PSST) has a demonstrated potential to improve psychosocial well-being for parents of children with chronic health conditions (CHCs), but such evidence has not been fully systematically synthesized. Objective: To evaluate the associations of PSST with parental, pediatric, and family psychosocial outcomes. Data Sources: Six English-language databases (PubMed, Embase, CINAHL, PsycINFO, Web of Science, and Cochrane Library), 3 Chinese-language databases (China National Knowledge Infrastructure, China Science and Technology Journal Database, and Wanfang), gray literature, and references were searched from inception to April 30, 2023. Study Selection: Randomized clinical trials (RCTs) that performed PSST for parents of children with CHCs and reported at least 1 parental, pediatric, or family psychosocial outcome were included. Data Extraction and Synthesis: Study selection, data extraction, and quality assessment were conducted independently by 2 reviewers. Data were pooled for meta-analysis using the standardized mean difference (SMD) by the inverse variance method or a random-effects model. Subgroup analyses of children- and intervention-level characteristics were conducted. Main Outcomes and Measures: The psychosocial outcomes of the parents, their children, and their families, such as problem-solving skills, negative affectivity, quality of life (QOL), and family adaptation. Results: The systematic review included 23 RCTs involving 3141 parents, and 21 of these trials were eligible for meta-analysis. There was a significant association between PSST and improvements in parental outcomes, including problem-solving skills (SMD, 0.43; 95% CI, 0.27-0.58), depression (SMD, -0.45; 95% CI, -0.66 to -0.23), distress (SMD, -0.61; 95% CI, -0.81 to -0.40), posttraumatic stress (SMD -0.39; 95% CI, -0.48 to -0.31), parenting stress (SMD, -0.62; 95% CI, -1.05 to -0.19), and QOL (SMD, 0.45; 95% CI, 0.15-0.74). For children, PSST was associated with better QOL (SMD, 0.76; 95% CI, 0.04-1.47) and fewer mental problems (SMD, -0.51; 95% CI, -0.68 to -0.34), as well as with less parent-child conflict (SMD, -0.38; 95% CI, -0.60 to -0.16). Subgroup analysis showed that PSST was more efficient for parents of children aged 10 years or younger or who were newly diagnosed with a CHC. Significant improvements in most outcomes were associated with PSST delivered online. Conclusions and Relevance: These findings suggest that PSST for parents of children with CHCs may improve the psychosocial well-being of the parents, their children, and their families. Further high-quality RCTs with longer follow-up times and that explore physical and clinical outcomes are encouraged to generate adequate evidence.

Application of Artificial Intelligence in Oncology Nursing: A Scoping Review.

BACKGROUND: Artificial intelligence (AI) has been increasingly used in healthcare during the last decade, and recent applications in oncology nursing have shown great potential in improving care for patients with cancer. It is timely to comprehensively synthesize knowledge about the progress of AI technologies in oncology nursing. OBJECTIVE: The aims of this study were to synthesize and evaluate the existing evidence of AI technologies applied in oncology nursing. METHODS: A scoping review was conducted based on the methodological framework proposed by Arksey and O'Malley and later improved by the Joanna Briggs Institute. Six English databases and 3 Chinese databases were searched dating from January 2010 to November 2022. RESULTS: A total of 28 articles were included in this review-26 in English and 2 in Chinese. Half of the studies used a descriptive design (level VI). The most widely used AI technologies were hybrid AI methods (28.6%) and machine learning (25.0%), which were primarily used for risk identification/prediction (28.6%). Almost half of the studies (46.4%) explored developmental stages of AI technologies. Ethical concerns were rarely addressed. CONCLUSIONS: The applicability and prospect of AI in oncology nursing are promising, although there is a lack of evidence on the efficacy of these technologies in practice. More randomized controlled trials in real-life oncology nursing settings are still needed. IMPLICATIONS FOR PRACTICE: This scoping review presents comprehensive findings for consideration of translation into practice and may provide guidance for future AI education, research, and clinical implementation in oncology nursing.