Effects of a Serious Smartphone Game on Nursing Students' Theoretical Knowledge and Practical Skills in Adult Basic Life Support: Randomized Wait List-Controlled Trial.

BACKGROUND: Retention of adult basic life support (BLS) knowledge and skills after professional training declines over time. To combat this, the European Resuscitation Council and the American Heart Association recommend shorter, more frequent BLS sessions. Emphasizing technology-enhanced learning, such as mobile learning, aims to increase out-of-hospital cardiac arrest (OHCA) survival and is becoming more integral in nursing education. OBJECTIVE: The aim of this study was to investigate whether playing a serious smartphone game called MOBICPR at home can improve and retain nursing students' theoretical knowledge of and practical skills in adult BLS. METHODS: This study used a randomized wait list-controlled design. Nursing students were randomly assigned in a 1:1 ratio to either a MOBICPR intervention group (MOBICPR-IG) or a wait-list control group (WL-CG), where the latter received the MOBICPR game 2 weeks after the MOBICPR-IG. The aim of the MOBICPR game is to engage participants in using smartphone gestures (eg, tapping) and actions (eg, talking) to perform evidence-based adult BLS on a virtual patient with OHCA. The participants' theoretical knowledge of adult BLS was assessed using a questionnaire, while their practical skills were evaluated on cardiopulmonary resuscitation quality parameters using a manikin and a checklist. RESULTS: In total, 43 nursing students participated in the study, 22 (51%) in MOBICPR-IG and 21 (49%) in WL-CG. There were differences between the MOBICPR-IG and the WL-CG in theoretical knowledge (P=.04) but not in practical skills (P=.45) after MOBICPR game playing at home. No difference was noted in the retention of participants' theoretical knowledge and practical skills of adult BLS after a 2-week break from playing the MOBICPR game (P=.13). Key observations included challenges in response checks with a face-down manikin and a general neglect of safety protocols when using an automated external defibrillator. CONCLUSIONS: Playing the MOBICPR game at home has the greatest impact on improving the theoretical knowledge of adult BLS in nursing students but not their practical skills. Our findings underscore the importance of integrating diverse scenarios into adult BLS training. TRIAL REGISTRATION: ClinicalTrials.gov (NCT05784675); https://clinicaltrials.gov/study/NCT05784675.

Person-Centred Care: A Support Strategy for Managing Non-Communicable Diseases.

BACKGROUND: Over the last decade, the inadequacy and unsustainability of current healthcare services for managing long-term co-morbid and multi-morbid diseases have become evident. METHODS: This study, involving 426 adults with at least one non-communicable disease in Slovenia, aimed to explore the link between quality of life, life satisfaction, person-centred care, and non-communicable disease management. RESULTS: Results indicated generally positive perceptions of quality of life, general health, and life satisfaction of individuals with non-communicable diseases. Participants assessed their physical health as the highest of the four quality of life domains, followed by the environment, social relations, and psychological health. Significant differences occurred in life satisfaction, general health, quality of life, and person-centred care for managing non-communicable diseases. But, there were no significant differences in person-centred care according to the living environment. The study revealed a positive association between person-centred care and effective non-communicable disease management, which is also positively associated with quality of life, general health, and life satisfaction. CONCLUSIONS: Person-centred care is currently the most compassionate and scientific practice conceived, representing a high ethical standard. However, implementing this approach in healthcare systems requires a cohesive national strategy led by capable individuals to foster stakeholder collaboration. Such an approach is crucial to address the deficiencies of existing healthcare services and ensure person-centred care sustainability in non-communicable disease management.

Psychometric Testing of the Slovene Version of the Perceived Inventory of Technological Competency as Caring in Nursing.

The Perceived Inventory of Technological Competency as Caring in Nursing (PITCCN) questionnaire has been designed to measure technological competency as caring in nursing practice. It incorporates the use of technology with the fundamental principles of caring that are central to nursing. As there were no psychometrically sound instruments to quantify the concept of technological competency as caring in the Slovene language, we adapted the English version of the questionnaire to the local environment. The goal was to assess the level of psychometric properties of the PITCCN investigated in Slovene hospitals. METHODS: Content validity was conducted with eight experts and quantified by the content validity index (CVI) and the modified Cohen's kappa index. Face validity was assessed through discussions with participants from the target culture in the pilot study. To assess construct validity and internal consistency, a cross-sectional research methodology was used on a convenience sample of 121 nursing personnel from four hospitals. Principal component analysis (PCA) was used to examine construct validity, while Cronbach's alpha and adjusted item-total correlations were used to measure internal consistency. RESULTS: The content and face validity of PITCCN were adequate. The scale validity index (S-CVI) was 0.97. Cronbach's α was 0.92, and subscale reliabilities ranged from 0.810 to 0.925. PCA showed four components, which explained more than 73.49% of the variance. CONCLUSIONS: The Slovenian version of PITCCN (PITCCN_SI) has good psychometric properties.

The ChatGPT Effect: Nursing Education and Generative Artificial Intelligence.

This article examines the potential of generative artificial intelligence (AI), such as ChatGPT (Chat Generative Pre-trained Transformer), in nursing education and the associated challenges and recommendations for their use. Generative AI offers potential benefits such as aiding students with assignments, providing realistic patient scenarios for practice, and enabling personalized, interactive learning experiences. However, integrating generative AI in nursing education also presents challenges, including academic integrity issues, the potential for plagiarism and copyright infringements, ethical implications, and the risk of producing misinformation. Clear institutional guidelines, comprehensive student education on generative AI, and tools to detect AI-generated content are recommended to navigate these challenges. The article concludes by urging nurse educators to harness generative AI's potential responsibly, highlighting the rewards of enhanced learning and increased efficiency. The careful navigation of these challenges and strategic implementation of AI is key to realizing the promise of AI in nursing education. [J Nurs Educ. 2024;63(X):XXX-XXX.].

Using generative artificial intelligence in bibliometric analysis: 10 years of research trends from the European Resuscitation Congresses.

Aims: The aim of this study is to use generative artificial intelligence to perform bibliometric analysis on abstracts published at European Resuscitation Council (ERC) annual scientific congress and define trends in ERC guidelines topics over the last decade. Methods: In this bibliometric analysis, the WebHarvy software (SysNucleus, India) was used to download data from the Resuscitation journal's website through the technique of web scraping. Next, the Chat Generative Pre-trained Transformer 4 (ChatGPT-4) application programming interface (Open AI, USA) was used to implement the multinomial classification of abstract titles following the ERC 2021 guidelines topics. Results: From 2012 to 2022 a total of 2491 abstracts have been published at ERC congresses. Published abstracts ranged from 88 (in 2020) to 368 (in 2015). On average, the most common ERC guidelines topics were Adult basic life support (50.1%), followed by Adult advanced life support (41.5%), while Newborn resuscitation and support of transition of infants at birth (2.1%) was the least common topic. The findings also highlight that the Basic Life Support and Adult Advanced Life Support ERC guidelines topics have the strongest co-occurrence to all ERC guidelines topics, where the Newborn resuscitation and support of transition of infants at birth (2.1%; 52/2491) ERC guidelines topic has the weakest co-occurrence. Conclusion: This study demonstrates the capabilities of generative artificial intelligence in the bibliometric analysis of abstract titles using the example of resuscitation medicine research over the last decade at ERC conferences using large language models.

Nurses' occupational physical activity and workload in a perioperative intensive care unit in Slovenia.

The field of nursing includes heavy occupational physical demands, including walking and standing for longer periods of time, in addition to moving and lifting. As such, in the context of a typical work shift, many nurses generally achieve the World Health Organization's recommended 10,000 steps per day. This study aimed at estimating the daily physical activity and workload of nurses in a perioperative intensive care unit. The data sources for this study included data from the hospital information system on various procedures and interventions, and the Silva Ex3 Plus pedometers for measuring steps, kilometers, calories, and activity time across various shifts in a perioperative intensive care unit. Twenty nurses from Slovenia volunteered to participate in this observational study. Over 13 weeks, a nurse working an 8-hour shift walked an average of 5,938 steps (4.4 km). However, nurses who worked a 12-hour weekend day shift came very close to the World Health Organization's recommendation with an average of 9,003 steps (6.5 km). A total of 227 patients were admitted and an average of 80 nursing interventions were performed per day and there was a positive relationship between physical activity, workload, and patient admissions in the perioperative intensive care unit (p = 0.001). Results of this study could help managers better understand nurses' physical activity and workload during various shifts in the perioperative intensive care unit.

Cognitive and Emotional Perceptions of Illness in Patients Diagnosed with Type 2 Diabetes Mellitus.

Type 2 diabetes mellitus (T2DM) affects a patient's physical, social, and mental well-being. Perceptions of the illness are linked to quality of life. The aim of this study was to assess illness perception in patients diagnosed with T2DM and to validate the Brief Illness Perception Questionnaire in the Slovenian language. A cross-sectional study involved 141 patients diagnosed with T2DM. We performed a content analysis of the questionnaire and estimated the S-CVI, I-CVI, kappa coefficient. We also used Cronbach's alpha to assess the reliability. Participants did not have a very threatening perception of T2DM, but being overweight and having cardiovascular disease were significant contributors to a more threatening perception. The most frequently indicated factors influencing the onset and development of T2DM were heredity and genetics, stress and other psychological distress, and poor and inadequate nutrition. I-CVI ranged from 0.833 to 1.00, while the kappa is greater than 0.74, confirming the excellent validity of the questions. The content validity assessment of the questionnaire further confirms that the questionnaire is suitable for use with the target population in Slovenia. The questionnaire proved to be a valid and reliable tool that can be used to assess the relationship between illness perception and self-management of T2DM.

The ChatGPT effect and transforming nursing education with generative AI: Discussion paper.

AIM: The aim of this study is to present the possibilities of nurse education in the use of the Chat Generative Pre-training Transformer (ChatGPT) tool to support the documentation process. BACKGROUND: The success of the nursing process is based on the accuracy of nursing diagnoses, which also determine nursing interventions and nursing outcomes. Educating nurses in the use of artificial intelligence in the nursing process can significantly reduce the time nurses spend on documentation. DESIGN: Discussion paper. METHODS: We used a case study from Train4Health in the field of preventive care to demonstrate the potential of using Generative Pre-training Transformer (ChatGPT) to educate nurses in documenting the nursing process using generative artificial intelligence. Based on the case study, we entered a description of the patient's condition into Generative Pre-training Transformer (ChatGPT) and asked questions about nursing diagnoses, nursing interventions and nursing outcomes. We further synthesized these results. RESULTS: In the process of educating nurses about the nursing process and nursing diagnosis, Generative Pre-training Transformer (ChatGPT) can present potential patient problems to nurses and guide them through the process from taking a medical history, setting nursing diagnoses and planning goals and interventions. Generative Pre-training Transformer (ChatGPT) returned appropriate nursing diagnoses, but these were not in line with the North American Nursing Diagnosis Association - International (NANDA-I) classification as requested. Of all the nursing diagnoses provided, only one was consistent with the most recent version of the North American Nursing Diagnosis Association - International (NANDA-I). Generative Pre-training Transformer (ChatGPT) is still not specific enough for nursing diagnoses, resulting in incorrect answers in several cases. CONCLUSIONS: Using Generative Pre-training Transformer (ChatGPT) to educate nurses and support the documentation process is time-efficient, but it still requires a certain level of human critical-thinking and fact-checking.

Prevalence of mental disorders and high rates of absenteeism from work among healthcare professionals in Slovenia: a retrospective study.

OBJECTIVES: To investigate the prevalence of mental disorders and the higher rates of absenteeism from work among healthcare workers employed in Slovenia by analysing the prevalence of sick leave and medication prescriptions for treatment of mental health and behavioural disorders from 2015 to 2020. DESIGN: Retrospective analysis of nationwide data on absenteeism and prescription of medications for treatment of mental health and behavioural disorders (anxiolytics, antipsychotics, antidepressants). SETTING: National databases of the National Institute of Public Health in Slovenia. PARTICIPANTS: All employed healthcare workers (35 008 in December 2020): dentists, midwives, nurses, nursing assistants, pharmacists and physicians in Slovenia from 2015 to 2020. RESULTS: The most time spent on sick leave by male healthcare workers aged >50 was for 'neoplasms' (71.50 days on average), followed by 'mental health and behavioural disorders' (62.08 days on average). Female healthcare workers under 40 years old spent the most time on sick leave for 'pregnancy, childbirth, and the postpartum period (puerperium)', causing an average of 58.38 days of sick leave. From 2015 to 2020, the highest increase in prescribed medications for treatment of mental health and behavioural disorders was among nursing assistants (an increase of 38.42%), pharmacists (an increase of 29.36%) and nurses (an increase of 26.61%); since the COVID-19 pandemic, an increase of 12.36% was found among dentists, an increase of 11.51% among pharmacists and an increase of 11.36% among nurses. CONCLUSION: The prescription of medications for treatment of mental health and behavioural disorders was on the rise from 2015 to 2020. The importance of employee health to individuals and society necessitates the systematisation of effective prevention programmes as well as programmes to assist those in need, especially health workers, whose work contributes significantly to maintaining public health.

E-Learning in Nursing and Midwifery during the COVID-19 Pandemic.

As the COVID-19 pandemic continues to spread, e-learning has increased. This is a challenge for nursing and midwifery students, as clinical training is an essential part of their education. The aim of this review was to identify the advantages and limitations of e-learning for nursing and midwifery students during the COVID-19 pandemic. A systematic review of the literature was conducted following the PRISMA guidelines. The international databases PubMed, CINAHL/MEDLINE, Web of Science, and ScienceDirect were searched. Articles were critically appraised. Thematic analysis was used to synthesise the data. The search resulted in 91 hits. Thirteen studies were included in the final analysis. Three main themes were identified: (1) the benefits of e-learning; (2) the challenges/limitations of e-learning; and (3) recommendations for e-learning. E-learning in nursing and midwifery is an effective alternative learning process during the COVID-19 pandemic. Students perceive several benefits and challenges related to internet access, technical equipment, financial aspects, and work and family commitments.

Enriching Real-world Data with Social Determinants of Health for Health Outcomes and Health Equity: Successes, Challenges, and Opportunities.

OBJECTIVE: To summarize the recent methods and applications that leverage real-world data such as electronic health records (EHRs) with social determinants of health (SDoH) for public and population health and health equity and identify successes, challenges, and possible solutions. METHODS: In this opinion review, grounded on a social-ecological-model-based conceptual framework, we surveyed data sources and recent informatics approaches that enable leveraging SDoH along with real-world data to support public health and clinical health applications including helping design public health intervention, enhancing risk stratification, and enabling the prediction of unmet social needs. RESULTS: Besides summarizing data sources, we identified gaps in capturing SDoH data in existing EHR systems and opportunities to leverage informatics approaches to collect SDoH information either from structured and unstructured EHR data or through linking with public surveys and environmental data. We also surveyed recently developed ontologies for standardizing SDoH information and approaches that incorporate SDoH for disease risk stratification, public health crisis prediction, and development of tailored interventions. CONCLUSIONS: To enable effective public health and clinical applications using real-world data with SDoH, it is necessary to develop both non-technical solutions involving incentives, policies, and training as well as technical solutions such as novel social risk management tools that are integrated into clinical workflow. Ultimately, SDoH-powered social risk management, disease risk prediction, and development of SDoH tailored interventions for disease prevention and management have the potential to improve population health, reduce disparities, and improve health equity.

Hybrid visualization-based framework for depressive state detection and characterization of atypical patients.

INTRODUCTION: Depression is a global concern, with a significant number of people affected worldwide, particularly in low- and middle-income countries. The rising prevalence of depression emphasizes the importance of early detection and understanding the origins of such conditions. OBJECTIVE: This paper proposes a framework for detecting depression using a hybrid visualization approach that combines local and global interpretation. This approach aims to assist in model adaptation, provide insights into patient characteristics, and evaluate prediction model suitability in a different environment. METHODS: This study utilizes R programming language with the Caret, ggplot2, Plotly, and Dalex libraries for model training, visualization, and interpretation. Data from the NHANES repository was used for secondary data analysis. The NHANES repository is a comprehensive source for examining health and nutrition of individuals in the United States, and covers demographic, dietary, medication use, lifestyle choices, reproductive and mental health data. Penalized logistic regression models were built using NHANES 2015-2018 data, while NHANES 2019-March 2020 data was used for evaluation at the global-specific and local level interpretation. RESULTS: The prediction model that supports this framework achieved an average AUC score of 0.748 (95% CI: 0.743-0.752), with minimal variability in sensitivity and specificity. CONCLUSION: The built-in prediction model highlights chest pain, the ratio of family income to poverty, and smoking status as crucial features for predicting depressive states in both the original and local environments.

Age, quality of life and mental well-being in adolescent population: a network model tree analysis.

This study presents the results of a network-based analysis of health related quality of life (HRQoL) among Slovenian adolescents. The study aimed to examine the relationship between HRQoL and mental well-being among adolescents of different age and gender groups. A cross-sectional study was conducted from November 2019 to January 2020 in 16 primary and 9 secondary schools in Slovenia. The KIDSCREEN-27 scale was used to collect the data on HRQoL, and the Warwick-Edinburgh Mental Well-being Scale to collect data on mental well-being. We used network model trees to demonstrate differences in psychometric network structure measuring correlations between different concepts in adolescent HRQoL. A total of 2972 students aged 10-19 years participated in the study. The significant split in the network tree (p < 0.001) indicated differences in relations between HRQoL subscale scores and mental well-being score among adolescents younger than 12 years old. In comparison to older adolescents the correlation between mental well-being and mood scores was significantly weaker in this group of the youngest participants (p < 0.001). A network model tree analysis also uncovered an interesting pattern based on gender and age (p < 0.013) where a correlation between mood and family support became weaker for female at the age of 12 and for male at the age of 16. Data mining techniques have recently been used by healthcare researchers and professionals. Network-based analysis is an innovative alternative to classical approaches in HRQoL research. In this study we demonstrate the significant differences in the perceptions of HRQoL and mental well-being among adolescents in different age and gender groups that were discovered using tree-based network analysis.

Using heterogeneous sources of data and interpretability of prediction models to explain the characteristics of careless respondents in survey data.

Prior to further processing, completed questionnaires must be screened for the presence of careless respondents. Different people will respond to surveys in different ways. Some take the easy path and fill out the survey carelessly. The proportion of careless respondents determines the survey's quality. As a result, identifying careless respondents is critical for the quality of obtained results. This study aims to explore the characteristics of careless respondents in survey data and evaluate the predictive power and interpretability of different types of data and indices of careless responding. The research question focuses on understanding the behavior of careless respondents and determining the effectiveness of various data sources in predicting their responses. Data from a three-month web-based survey on participants' personality traits such as honesty-humility, emotionality, extraversion, agreeableness, conscientiousness and openness to experience was used in this study. Data for this study was taken from Schroeders et al.. The gradient boosting machine-based prediction model uses data from the answers, time spent for answering, demographic information on the respondents as well as some indices of careless responding from all three types of data. Prediction models were evaluated with tenfold cross-validation repeated a hundred times. Prediction models were compared based on balanced accuracy. Models' explanations were provided with Shapley values. Compared with existing work, data fusion from multiple types of information had no noticeable effect on the performance of the gradient boosting machine model. Variables such as "I would never take a bribe, even if it was a lot", average longstring, and total intra-individual response variability were found to be useful in distinguishing careless respondents. However, variables like "I would be tempted to use counterfeit money if I could get away with it" and intra-individual response variability of the first section of a survey showed limited effectiveness. Additionally, this study indicated that, whereas the psychometric synonym score has an immediate effect and is designed with the goal of identifying careless respondents when combined with other variables, it is not necessarily the optimal choice for fitting a gradient boosting machine model.