Background: People with chronic obstructive pulmonary disease (COPD) are stigmatized by smoking history. Although little is known about COPD-related stigma, it can adversely affect self-management and quality of life. Objective: To synthesize relevant scientific literature exploring stigma experiences and their impacts on people with COPD. Methods: CINAHL/PsycINFO/PubMed/Scopus were searched for relevant studies. Findings were organized using Major et al’s conceptual model. Results: Fifteen studies documented COPD-related stigma processes: enacted, felt, internalized, and anticipated. Moderating factors included visibility, origin, and illness perception. Individual-level stigma responses included emotional distress, limited social interactions, and negative effects on medication adherence and help-seeking. Social/community-level stigma experiences included healthcare provider and employer behaviors. Smoking is interwoven throughout all domains of stigma processes and responses to stigma. Conclusion: Substantial evidence documents processes, moderating factors, and individual and social/community responses to the complex phenomenon of COPD-related stigma; however, prevalence of COPD-related stigma and its health effects are unclear.

Despite the harmful effect on health, e-cigarette and hookah smoking in youth in the U.S. has increased. Developing tailored e-cigarette and hookah cessation programs for youth is imperative. The aim of this study was to identify predictor variables such as social, mental, and environmental determinants that cause nicotine addiction in youth e-cigarette or hookah users and build nicotine addiction prediction models using machine learning algorithms. A total of 6511 participants were identified as ever having used e-cigarettes or hookah from the National Youth Tobacco Survey (2019) datasets. Prediction models were built by Random Forest with ReliefF and Least Absolute Shrinkage and Selection Operator (LASSO). ReliefF identified important predictor variables, and the Davies–Bouldin clustering evaluation index selected the optimal number of predictors for Random Forest. A total of 193 predictor variables were included in the final analysis. Performance of prediction models was measured by Root Mean Square Error (RMSE) and Confusion Matrix. The results suggested high performance of prediction. Identified predictor variables were aligned with previous research. The noble predictors found, such as ‘witnessed e-cigarette use in their household’ and ‘perception of their tobacco use’, could be used in public awareness or targeted e-cigarette and hookah youth education and for policymakers.

Objectives: Health systems that apply artificial intelligence (AI) are transforming the roles of healthcare providers, including those of Doctor of Nursing Practice (DNP) providers. These professionals are required to utilize informatics knowledge and skills to deliver quality care, necessitating a high level of informatics competencies, which should be developed through well-structured courses. The purpose of this study is to assess the informatics competency scale scores of DNP students and to provide recommendations for enhancing the informatics curriculum.Methods: An online informatics course was offered to students enrolled in a Bachelor of Science in Nursing to DNP program, and their informatics competency, which includes three subscales, was evaluated. Online survey data were collected from Fall 2021 to Fall 2022 using the “Self-Assessment of Informatics Competency Scale for Health Professionals.”Results: An analysis of 127 student responses revealed that students demonstrated competence in overall informatics competency and in one subscale: “applied computer skills (clinical informatics).” They showed proficiency in the “basic computer skills” and the “role” subscales. However, they reported lower competency in managing data and integrating standard terminology into their practice.Conclusions: The findings offer detailed insights into the current informatics competencies of DNP students and can inform informatics educators on how to enhance their courses. As healthcare institutions increasingly depend on AI applications, it is imperative for informatics educators to include AI-related content in their curricula.