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Aim: To map key characteristics and describe nurse led models of care for the treatment of persons with substance use disorders (SUDs) in the outpatient setting. Design: A scoping review. Methods: Conducted in accordance with the JBI methodology. The PRISMA‐ScR checklist was used. Data Sources: Pubmed, CINAHL Complete (EBSCOhost), Cochrane Library, APA PsycNet and Scopus were searched from 1999 to May 2022 and updated on 28 November 2023. A handsearch and a grey literature search was conducted. Results: Title and abstract screening was performed on 774 articles resulting in 88 articles for full text screening. Full text screening yielded 13 articles that met inclusion criteria. Conclusion: Existing nurse‐led models of care for SUDS are scarce and limited in scope, with the majority focused on treating opioid use disorder. Additional research is needed to develop, test, and implement efficacious nurse‐led models of care for the treatment and management of SUDs. Implications to Clinical Practice: Nurse led models of care have demonstrated their efficacy and quality in the management of other chronic diseases. As we move forward with innovative solutions for individuals with addiction, nurse led models of care can be a mechanism to deliver high quality, evidence‐based care for SUDs. Impact: SUDs are chronic diseases that impact individuals, families, and communities. SUDs require a biopsychosocial approach to treatment. Globally, nurses are well positioned to provide high quality care to mitigate the impact of SUDs. This scoping review mapped the extant literature on nurse led models of care for substance use disorder treatment in the outpatient setting finding that additional research is needed to develop, test and implement evidence‐based interventions to care for individuals, families, and communities experiencing SUDs. Reporting Method: PRISMA checklist for scoping reviews. No patient or public contribution were part of this study. Protocol Registration: Open Science Framework accessible at: https://doi.org/10.17605/OSF.IO/NSW7V. [ABSTRACT FROM AUTHOR]

ABSTRACT In addiction treatment, harm reduction is a philosophy that aims to reduce the harms from ongoing alcohol and other drug use. Although abstinence may be the ‘gold standard’ in reducing harm from ongoing alcohol and other drug use, harm reduction recognises that abstinence may not be achievable for certain individuals. Accordingly, harm reduction is used to enable medical or mental health treatment for individuals who continue to use alcohol and other drugs, providing a form of care which meets individuals where they present to healthcare facilities. Harm reduction accepts ongoing alcohol and other drug use, while providing a traditionally marginalised cohort of individuals access to healthcare services. In this perspective paper, we argue that the role of nurses in promoting and utilising harm reduction as part of their regular practice is essential to both reducing harm from alcohol and other drug use, engaging individuals who use alcohol and other drugs in healthcare services, and providing a means to accept individuals as they are to build trust and rapport for engagement in addiction treatment when they are ready, and at their own pace. Nurses, by virtue of their role and number in the healthcare landscape (approximately 28 million globally), are ideally placed to implement harm reduction in their practice to achieve better outcomes for individuals who use alcohol and other drugs. [ABSTRACT FROM AUTHOR]

The article presents the need to leverage the nursing workforce in the U.S. to address the addiction crisis, highlighting the importance of expanding access to addiction treatment and the role of nurses in providing care to individuals with substance use disorders, including their expertise, reach, and efficacy.

The article presents the discussion on addiction being defined as a chronic, relapsing disorder characterised by compulsive drug. Topics include causing discrimination by both healthcare providers and society at large leading to reduced individual health-seeking behaviour; and providing several services including health teaching, mental and physical health support and addiction treatment.

This study investigated the recovery process for individuals engaged in treatment for substance use disorders (SUDs) who had co-occurring anxiety and depression. The participants were eight individuals engaged in treatment. The results of a Grounded Theory design and methods revealed the core category and substantive theory, Stumbling toward Vulnerability. Four phases in which the participants progressed in a linear way emerged. The study results have implications for the role of the advance practice psychiatric-mental health nurse in the early assessment of mental illness for clients with SUDs by providing integrated treatment for these individuals, and focusing on health and wellness as a recovery outcome. Based on the findings, hypotheses for further research are recommended. [ABSTRACT FROM AUTHOR]

Background: Qualitative methods analyze contextualized, unstructured data. These methods are time and cost intensive, often resulting in small sample sizes and yielding findings that are complicated to replicate. Integrating natural language processing (NLP) into a qualitative project can increase efficiency through time and cost savings; increase sample sizes; and allow for validation through replication. This study compared the findings, costs, and time spent between a traditional qualitative method (Investigator only) to a method pairing a qualitative investigator with an NLP function (Investigator +NLP). Methods: Using secondary data from a previously published study, the investigators designed an NLP process in Python to yield a corpus, keywords, keyword influence, and the primary topics. A qualitative researcher reviewed and interpreted the output. These findings were compared to the previous study results. Results: Using comparative review, our results closely matched the original findings. The NLP + Investigator method reduced the project time by a minimum of 120 hours and costs by $1,500. Discussion: Qualitative research can evolve by incorporating NLP methods. These methods can increase sample size, reduce project time, and significantly reduce costs. The results of an integrated NLP process create a corpus and code which can be reviewed and verified, thus allowing a replicable, qualitative study. New data can be added over time and analyzed using the same interpretation and identification. Off the shelf qualitative software may be easier to use, but it can be expensive and may not offer a tailored approach or easily interpretable outcomes which further benefits researchers. [ABSTRACT FROM AUTHOR]

Diversity, equity, inclusion, and antiracism (DEI-A) are critical to providing adequate health care to all populations. High-fidelity simulations and role-play scenarios allow students to experience caring for clients from diverse backgrounds. This article discusses the project development and implementation of a DEI-A simulation day placed in a community health clinical course. [ABSTRACT FROM AUTHOR]

As the demand for Psychiatric Mental Health Nurse Practitioners (PMHNPs) continues to grow, innovative teaching strategies that can enhance competency learning with skill transfer into the clinical setting is essential. Educational programs must adequately prepare the student for the complexity of the clinical environment. Simulation is an educational approach that replicates aspects of the real world in a safe environment so that learners can engage in an interactive manner. High-fidelity simulation (HFS) has been identified as an effective teaching strategy and mechanism to transfer knowledge and skills learned in the classroom to the clinical setting. HFS uses a human patient simulator to immerse the learner in a contextual learning environment by engaging in a realistic clinical scenario to develop cognitive, affective and psychomotor skills through deliberate practice. This article describes the theoretical foundation, development process, case scenarios, and implementation of HFS, an innovative teaching pedagogy, in a Psychopharmacology didactic course for PMHNP. [ABSTRACT FROM AUTHOR]

Addiction nurses are highly skilled providers of holistic care and ensuring workforce sustainability is key to providing quality care to a traditionally marginalised group of healthcare consumers. The aim of this study was to explore perceived stigma towards the addiction nursing speciality, addiction nursing (also known as alcohol and other drug nursing) and its impact on workforce sustainability, retention and recruitment. Secondary analysis of qualitative interview data with nurses (n = 50) and survey data (n = 337) was conducted as part of a workforce mapping exercise in 2019. COREQ reporting guidelines were used. After structural coding was applied, three themes emerged: stigma experienced by clients of alcohol and other drug treatment services, stigma experienced by addiction nurses and a lack of awareness of the specialty of addiction nursing itself. Participants overwhelmingly felt that these forms of stigma made addiction nursing less attractive to new entrants, particularly new nurses and posed a threat to the sustainability of the specialty. The findings from this study indicate that urgent attention is required to address stigma towards individuals who use alcohol and other drugs, and the nurses providing care for them. Furthermore, creating awareness of the addiction nursing specialty is paramount to ensure workforce sustainability and to improve care for individuals who use alcohol and other drugs. Beyond addiction nurses, our results indicate that stigma towards other specialties (such as mental health nursing) is a substantive barrier to workforce sustainability. [ABSTRACT FROM AUTHOR]

Substance Use Disorders (SUDs) are a national public health crisis. However, there is minimal existing research literature on the role of the nurse working in SUD treatment. The purpose of this study was to describe the meaning of the professional role of the registered nurse working in the SUD setting. The study utilized a Heideggerian Phenomenological approach with a modified Colaizzi method for analysis and interpretation. The nine study participants, two males and seven females, aged 27-60 had worked in SUD treatment for 1-37 years. Three major themes, with sub-themes, emerged: Defining the Role for Self; Learning the Role; and Navigating with Ease in an Unchangeable Culture. Findings demonstrated that nurses in SUD treatment value their role and the care of patients with SUDs. Patient recovery or relapse influenced job satisfaction. Significantly, the findings highlighted an outdated role with rigid boundaries and no real contemporary identity. Findings can be used to address the need for a contemporary SUD nursing identity and to support ongoing involvement of nurses in health policy related to SUDs. [ABSTRACT FROM AUTHOR]

Aim: To identify and map out existing nurse‐led models of care for treatment and prevention of metabolic syndrome in primary care settings. Design: A scoping review. Methods: Conducted in accordance with the JBI methodology. Data Sources: A search of the databases PubMed, CINAHL Complete, Cochrane Library, Scopus, handsearch and a grey literature search was conducted in June 2022 and updated in March 2023. Results: Title and abstract screening was performed on 926 articles resulting in 40 articles for full text screening. Full text screening yielded seven articles that met inclusion criteria. Conclusion: Additional research is needed on nursing models of care to prevent and treat metabolic syndrome. Future studies should concentrate on rigour with clearly defined objective inclusion criteria. Implications to Clinical Practice: This review contributes a synthesis of the evidence on nurse‐led models for metabolic syndrome in primary care. Impact: This scoping review addresses metabolic syndrome, the precursor to non‐communicable disease. The review mapped the evidence for nurse‐led models of care for metabolic syndrome in the primary care setting. These findings promote the development and evaluation of novel nurse‐led models of care which can mitigate the effect of the current epidemic. Reporting Method: PRISMA checklist for scoping reviews. No patient or public contribution was part of this study. Protocol Registration: Open Science Framework accessible at: https://osf.io/jfpw7/. [ABSTRACT FROM AUTHOR]

Background: Electronic health systems contain large amounts of unstructured data (UD) which are often unanalyzed due to the time and costs involved. Unanalyzed data creates missed opportunities to improve health outcomes. Natural language processing (NLP) is the foundation of generative artificial intelligence (GAI), which is the basis for large language models, such as ChatGPT. NLP and GAI are machine learning methods that analyze large amounts of data in a short time at minimal cost. The ability of NLP to conduct qualitative analyses is increasing, yet the results can lack context and nuance in their findings, requiring human intervention. Methods: Our study compared outcomes, time, and costs of a previously published qualitative study. Our approach partnered an NLP model and a qualitative researcher (NLP+). UD from behavioral health patients were analyzed using NLP and a Latent Dirichlet allocation to identify the topics using probability of word coherence scores. The topics were then analyzed by a qualitative researcher, translated into themes, and compared with the original findings. Results: The NLP + method results aligned with the original, qualitative derived themes. Our model also identified two additional themes which were not originally detected. The NLP + method required 6 hours of labor, 3 minutes for transcription, and a transcription cost of $1.17. The original, qualitative researcher only method required more than 36 hours ($2,250) of time and $1,100 for transcription. Conclusions: While natural language processing analyzes voluminous amounts of data in seconds, context and nuance in human language are regularly missed. Combining a qualitative researcher with NLP + could be deployed in many settings, reducing time and costs, and improving context. Until large language models are more prevalent, a human interaction can help translate the patient experience by contextualizing data rich in social determinant indicators which may otherwise go unanalyzed. [ABSTRACT FROM AUTHOR]