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Aim This study identified and evaluated tested patient safety educational interventions. This study also described the content, curricular structures and teaching strategies of the educational interventions and determined the methods used for evaluating patient safety learning outcomes. Design The Preferred Reporting Items for Systematic Reviews and Meta‐Analyses guidelines directed this review. Methods Searches for articles describing and evaluating patient safety educational interventions were conducted using four scholarly databases. Study quality was assessed using the McMaster Critical Review Form. Results Seven studies met the inclusion criteria. Educational interventions were either presented as stand‐alone courses or as lessons embedded in an existing course. All studies employed a mixture of various teaching modalities and several evaluation methods and outcomes. Mixed results were observed in terms of the effects of educational interventions. Future researchers should continue to develop patient safety curricula and examine their effect on student competencies with stronger methodological rigour.

OBJECTIVESThe aims of this study were to examine the relationship between 1-year retention of newly licensed RNs (NLRNs) employed in hospitals and personal and hospital characteristics, and determine which characteristics had the most influence. METHODSA secondary analysis of data collected in a study of transition to practice was used to describe the retention of 1464 NLRNs employed by 97 hospitals in 3 states. Hospitals varied in size, location (urban and rural), Magnet® designation, and university affiliation. The NLRNs also varied in education, age, race, gender, and experience. RESULTSThe overall retention rate at 1 year was 83%. Retention of NLRNs was higher in urban areas and in Magnet hospitals. The only personal characteristic that affected retention was age, with younger nurses more likely to stay. CONCLUSIONHospital characteristics had a larger effect on NLRN retention than personal characteristics. Hospitals in rural areas have a particular challenge in retaining NLRNs.

Aim To explore the strategies used by nursing students in passing the nursing licensure examination. Design This study uses a classic grounded theory design to explore the social processes influencing a nursing license examination. Methods Eight graduate students participated in this research study and were interviewed in‐depth twice. The Classic Grounded Theory method of Glaser was applied to collect and analyse the data until saturation was reached. Results The findings revealed that students who passed the nursing licensure examination described the strategies as a preliminary model comprising a core category, Reviewing (Phase 1), which consisted of two sub‐categories: Entering Time and Reviewing Styles. Additionally, two other main categories emerged: the Tutoring category (Phase 2) and the Testing Practice category (Phase 3). It was observed that each course (subject) does not necessarily follow a specific order in traversing these phases; they may move back and forth between them until the conclusion of the examination. Furthermore, it was found that the time allocated to Entering Time and completing the three phases significantly influences the successful passing of the nursing licensure examination.

The United States’ nursing shortage attracted internationally educated nurses (IENs) to take the National Council Licensure Examination–Registered Nurses (NCLEX-RN), which is required to practice nursing in the U.S. Philippine-educated nurses (PENs) comprised more than half of IENs in the U.S. nursing workforce. From 2002 to 2021, only 45.8% of 177,730 PENs passed the exam. Published studies investigating IEN NCLEX-RN performance are limited. This study addresses this gap in the literature. This study determined the association between academic, initial nursing licensure, and employment factors on PEN NCLEX-RN pass rates. A retrospective correlation research design was used to determine the association among the research variables. Participants were recruited through online nursing groups. Descriptive statistics compared characteristics of PENs who passed or failed the NCLEX on the first attempt. Chi-squared and Fisher’s exact tests were used to determine the association between the research variables. Initial nursing licensure and nursing workplace were significantly associated with PENs passing the NCLEX-RN. Identifying unique PENs’ contextual characteristics is critical in preparing them to pass the NCLEX-RN. Findings provide input to educational and regulatory bodies to improve the NCLEX-RN individual outcomes and Philippine NCLEX-RN pass rates.

The creation of large language models (LLMs) such as ChatGPT is an important step in the development of artificial intelligence, which shows great potential in medical education due to its powerful language understanding and generative capabilities. The purpose of this study was to quantitatively evaluate and comprehensively analyze ChatGPT's performance in handling questions for the National Nursing Licensure Examination (NNLE) in China and the United States, including the National Council Licensure Examination for Registered Nurses (NCLEX-RN) and the NNLE. This study aims to examine how well LLMs respond to the NCLEX-RN and the NNLE multiple-choice questions (MCQs) in various language inputs. To evaluate whether LLMs can be used as multilingual learning assistance for nursing, and to assess whether they possess a repository of professional knowledge applicable to clinical nursing practice. First, we compiled 150 NCLEX-RN Practical MCQs, 240 NNLE Theoretical MCQs, and 240 NNLE Practical MCQs. Then, the translation function of ChatGPT 3.5 was used to translate NCLEX-RN questions from English to Chinese and NNLE questions from Chinese to English. Finally, the original version and the translated version of the MCQs were inputted into ChatGPT 4.0, ChatGPT 3.5, and Google Bard. Different LLMs were compared according to the accuracy rate, and the differences between different language inputs were compared. The accuracy rates of ChatGPT 4.0 for NCLEX-RN practical questions and Chinese-translated NCLEX-RN practical questions were 88.7% (133/150) and 79.3% (119/150), respectively. Despite the statistical significance of the difference (P=.03), the correct rate was generally satisfactory. Around 71.9% (169/235) of NNLE Theoretical MCQs and 69.1% (161/233) of NNLE Practical MCQs were correctly answered by ChatGPT 4.0. The accuracy of ChatGPT 4.0 in processing NNLE Theoretical MCQs and NNLE Practical MCQs translated into English was 71.5% (168/235; P=.92) and 67.8% (158/233; P=.77), respectively, and there was no statistically significant difference between the results of text input in different languages. ChatGPT 3.5 (NCLEX-RN P=.003, NNLE Theoretical P<.001, NNLE Practical P=.12) and Google Bard (NCLEX-RN P<.001, NNLE Theoretical P<.001, NNLE Practical P<.001) had lower accuracy rates for nursing-related MCQs than ChatGPT 4.0 in English input. English accuracy was higher when compared with ChatGPT 3.5's Chinese input, and the difference was statistically significant (NCLEX-RN P=.02, NNLE Practical P=.02). Whether submitted in Chinese or English, the MCQs from the NCLEX-RN and NNLE demonstrated that ChatGPT 4.0 had the highest number of unique correct responses and the lowest number of unique incorrect responses among the 3 LLMs. This study, focusing on 618 nursing MCQs including NCLEX-RN and NNLE exams, found that ChatGPT 4.0 outperformed ChatGPT 3.5 and Google Bard in accuracy. It excelled in processing English and Chinese inputs, underscoring its potential as a valuable tool in nursing education and clinical decision-making.

Purpose: The Korean Nursing Licensing Examination (KNLE) is planning to transition to a computer-based test (CBT). This study aims to propose a reasonable and efficient method for setting passing scores.Methods: A standard setting (passing score setting) analysis was conducted using an expert panel over the past 3 years of the national nursing examination. The standard-setting method was modified from Angoff, and the validity of the passing score was verified through the Hofstee method. The standard-setting workshop was conducted in 2 stages: first, a pilot workshop for 2 subjects, followed by a second workshop where 6 additional subjects were selected based on the pilot results. For items with an actual correct answer rate of 90% or higher, the estimated correct answer rate for minimum competency was calculated using the observed correct answer rate. A survey and discussion with the expert panel were also conducted regarding the standard-setting procedures and results.Results: The passing score for the national nursing examination was calculated using the new method, and the score was slightly higher than the existing score. The nursing subject had similar results,; however, the legal subjects varied.Conclusion: The modified Angoff and Hofstee methods were successfully applied to the KNLE. Using the actual correct answer rate as an indicator to derive expected minimum competency was shown to be effective. This approach could streamline future standard-setting processes, particularly when converting to CBT.

Large language models (LLMs) have been proposed as valuable tools in medical education and practice. The Chinese National Nursing Licensing Examination (CNNLE) presents unique challenges for LLMs due to its requirement for both deep domain-specific nursing knowledge and the ability to make complex clinical decisions, which differentiates it from more general medical examinations. However, their potential application in the CNNLE remains unexplored. This study aims to evaluates the accuracy of 7 LLMs including GPT-3.5, GPT-4.0, GPT-4o, Copilot, ERNIE Bot-3.5, SPARK, and Qwen-2.5 on the CNNLE, focusing on their ability to handle domain-specific nursing knowledge and clinical decision-making. We also explore whether combining their outputs using machine learning techniques can improve their overall accuracy. This retrospective cross-sectional study analyzed all 1200 multiple-choice questions from the CNNLE conducted between 2019 and 2023. Seven LLMs were evaluated on these multiple-choice questions, and 9 machine learning models, including Logistic Regression, Support Vector Machine, Multilayer Perceptron, k-nearest neighbors, Random Forest, LightGBM, AdaBoost, XGBoost, and CatBoost, were used to optimize overall performance through ensemble techniques. Qwen-2.5 achieved the highest overall accuracy of 88.9%, followed by GPT-4o (80.7%), ERNIE Bot-3.5 (78.1%), GPT-4.0 (70.3%), SPARK (65.0%), and GPT-3.5 (49.5%). Qwen-2.5 demonstrated superior accuracy in the Practical Skills section compared with the Professional Practice section across most years. It also performed well in brief clinical case summaries and questions involving shared clinical scenarios. When the outputs of the 7 LLMs were combined using 9 machine learning models, XGBoost yielded the best performance, increasing accuracy to 90.8%. XGBoost also achieved an area under the curve of 0.961, sensitivity of 0.905, specificity of 0.978, F -score of 0.901, positive predictive value of 0.901, and negative predictive value of 0.977. This study is the first to evaluate the performance of 7 LLMs on the CNNLE and that the integration of models via machine learning significantly boosted accuracy, reaching 90.8%. These findings demonstrate the transformative potential of LLMs in revolutionizing health care education and call for further research to refine their capabilities and expand their impact on examination preparation and professional training.

OBJECTIVETo identify how certification is defined, conceptualized, and discussed in the nursing literature. BACKGROUNDAlthough it is hypothesized that credentialing is associated with better patient outcomes, the evidence is relatively limited. Some authors have suggested that the lack of consistency used to define certification in nursing literature may be one of the dominant obstacles in credentialing research. METHODSThis scoping review was guided by Arksey and OʼMalleyʼs framework, and quantitative and qualitative analyses were conducted. RESULTSThe final data set contained a total of 36 articles, of which 14 articles provided a referenced definition of certification. Thematic analysis of the definitions yielded 8 dominant themes. CONCLUSIONThe lack of a common definition of certification in nursing must be addressed to advance research into the relationship between certification processes in nursing and healthcare outcomes.

Background: The nursing shortage has been deemed a public health crisis as the turnover rate of newly licensed graduate nurses (NLGNs) continues to grow. One of five NLGNs are leaving the profession due to work dissatisfaction and feelings of inadequacy, risking patient safety. Method: A prospective, randomized controlled trial evaluated the impact of a 6-week digital intervention (text messaging) on NLGNs' self-reported stress, resiliency, sense of support, and intention to leave their jobs, organization, and profession. Messages to the experimental group (n = 10) conveyed emotional, esteem, and networking support, and messages to the control group (n = 11) were medical facts. Results: The digital intervention in the form of medical facts increased the control group's sense of social support. Stress, resilience, and intention to leave their jobs, organizations, or profession did not change for either the control or experimental group. Conclusion: A digital intervention, such as text messaging, potentially can increase NLGNs' sense of support during their first year of hire. [J Contin Educ Nurs. 2021;52(8):367–374.]

OBJECTIVEThe aim of this study was to describe differences in associate degree (ADN) and baccalaureate degree–prepared (BSN) nursesʼ perceptions of top-of-license (TOL) practice. BACKGROUNDTo date, no empirical work has examined whether ADN and BSN nurses approach TOL practice nursing activities differently. METHODSWe conducted a qualitative pilot study with focus groups to explore the perceptions of a group of ADN- and BSN-prepared nurses concerning nursing activities and their relation to TOL practice. RESULTSSubthemes emerged differentiating how ADN and BSN nurses perceived their responsibilities related to critical thinking, communication, and patient education. For professional nursing care, 5 subthemes further emerged(a) approaches to assessment, (b) chart review, (c) psychosocial patient care, (d) documentation, and (e) handoff. CONCLUSIONSThe differences identified in approaches to TOL practice activities by educational preparation have implications for staffing patterns that can optimize the contribution of ADN- and BSN-prepared nurses. Further research is indicated.