IA_RH

Machine learning applications to personnel selection: Current illustrations, lessons learned, and future research.

Fri, 01 Dec 2023 05:00:00 +0000

Personnel Psychology; 12/01/2023
(AN 173720435); ISSN: 00315826
Health Business Elite

A simulation of the impacts of machine learning to combine psychometric employee selection system predictors on performance prediction, adverse impact, and number of dropped predictors.

Fri, 01 Dec 2023 05:00:00 +0000

Personnel Psychology; 12/01/2023
(AN 173720429); ISSN: 00315826
Health Business Elite

Improving measurement and prediction in personnel selection through the application of machine learning.

Fri, 01 Dec 2023 05:00:00 +0000

Personnel Psychology; 12/01/2023
(AN 173720431); ISSN: 00315826
Health Business Elite

Reducing subgroup differences in personnel selection through the application of machine learning.

Fri, 01 Dec 2023 05:00:00 +0000

Personnel Psychology; 12/01/2023
(AN 173720430); ISSN: 00315826
Health Business Elite

Artificial Intelligence: Basics, Impact, and How Nurses Can Contribute.

Fri, 01 Dec 2023 05:00:00 +0000

Clinical Journal of Oncology Nursing; 12/01/2023
Applying artificial intelligence (AI) to cancer care has the potential to transform and enhance nursing practice and patient outcomes, from cancer prevention and screening through treatment, survivorship, and end-of-life care. As the largest healthcare workforce, nurses record a significant amount of patient data used to train healthcare AI tools and are a large percentage of AI end users. Educational opportunities are available to assist nurses in understanding the benefits, limitations, and ethical considerations of this technology and how AI results are directly affected by the quality of nursing documentation. Applying nursing clinical knowledge and critical thinking skills throughout the AI life cycle will enhance nursing workflows and increase positive patient outcomes.
(AN 173871707); ISSN: 10921095
CINAHL Complete

Artificial Intelligence and the Critical Care Nurse.

Fri, 01 Dec 2023 05:00:00 +0000

Critical Care Nurse; 12/01/2023
The article discusses the use of artificial intelligence (AI) technology in critical care nursing. Topics explored include the capability of AI tools to optimize patient care and improve clinical decision-making, the significant role of critical care nurses in the application of AI tools in the health care setting, and the need for nurses to consider their nursing knowledge and judgment in validating AI-produced recommendations.
(AN 173949076); ISSN: 02795442
CINAHL Complete

The Basics of Artificial Intelligence in Nursing: Fundamentals and Recommendations for Educators.

Fri, 01 Dec 2023 05:00:00 +0000

Journal of Nursing Education; 12/01/2023
Background: Artificial intelligence (AI) offers exciting possibilities; however, AI is a double-edged sword. The adoption of this technology offers many benefits but also presents risks to academic integrity and appropriately prepared graduates. Many of today's nurse educators are from generations that are unlikely to possess an understanding of AI. This article provides fundamental knowledge needed to understand the current state of AI in nursing and offers recommendations to nurse educators on ways to responsibly incorporate AI technologies into nursing curricula. Method: AI literature from PubMed, CINAHL, and Google Scholar was reviewed and synthesized. Results: Definitions, explanations, and applications to nursing education are outlined. Recommendations are made for AI implementation, along with ideas to avoid potential AI-enabled plagiarism and academic dishonesty. Conclusion: As professionals, nurse educators should understand the basics of AI and be able to judge the appropriateness of integration and also recognize opportunities to embrace future application. [J Nurs Educ. 2023;62(12):716–720.]
(AN 173992696); ISSN: 01484834
CINAHL Complete

Nursing research, practice, education, and artificial intelligence: What is our future?

Fri, 01 Dec 2023 05:00:00 +0000

Research in Nursing & Health; 12/01/2023
(AN 173470085); ISSN: 01606891
CINAHL Complete

Screening for Psychological Distress in Healthcare Workers Using Machine Learning: A Proof of Concept.

Thu, 16 Nov 2023 05:00:00 +0000

Journal of Medical Systems; 11/16/2023
The purpose of this study was to train and test preliminary models using two machine learning algorithms to identify healthcare workers at risk of developing anxiety, depression, and post-traumatic stress disorder. The study included data from a prospective cohort study of 816 healthcare workers collected using a mobile application during the first two waves of COVID-19. Each week, the participants responded to 11 questions and completed three screening questionnaires (one for anxiety, one for depression, and one for post-traumatic stress disorder). Then, the research team selected two questions (out of the 11), which were used with biological sex to identify whether scores on each screening questionnaire would be positive or negative. The analyses involved a fivefold cross-validation to test the accuracy of models based on logistic regression and support vector machines using cross-sectional and cumulative measures. The findings indicated that the models derived from the two questions and biological sex accurately identified screening scores for anxiety, depression, and post-traumatic stress disorders in 70% to 80% of cases. However, the positive predictive value never exceeded 50%, underlining the importance of collecting more data to train better models. Our proof of concept demonstrates the feasibility of using machine learning to develop novel models to screen for psychological distress in at-risk healthcare workers. Developing models with fewer questions may reduce burdens of active monitoring in practical settings by decreasing the weekly assessment duration.
(AN 173963178); ISSN: 01485598
CINAHL Complete

Factors influencing student nurses' readiness to adopt artificial intelligence (AI) in their studies and their perceived barriers to accessing AI technology: A cross-sectional study.

Wed, 01 Nov 2023 04:00:00 +0000

Nurse Education Today; 11/01/2023
With the projected significant increase in the use of AI in nursing education, it becomes vital for nurse faculty to adequately equip student nurses with the necessary competences to effectively utilize AI in their studies. Ensuring that student nurses are prepared and ready to embrace AI technology is imperative for their successful integration into the healthcare workforce. This study aimed to examine student nurses' readiness to embrace AI technology, explore associated factors, and identify perceived barriers to accessing AI technology. Cross sectional study. One public-owned nursing school in the Philippines. Three hundred twenty-three student nurses. Data were collected using structured questionnaires. Descriptive statistics and multivariable analysis were performed to analyze the data. The results revealed that student nurses demonstrated moderate readiness to embrace AI in their studies (M = 2.906, SD = 0.692) and perceived moderate barriers to accessing AI technology (M = 2.336, SD = 0.719). Factors associated with students' readiness to embrace AI included self-rated technological proficiency (β = 0.170, p = 0.014), understanding of AI-powered technologies (β = 0.260, p < 0.001), and perceived AI use in nursing practice (β = 0.153, p = 0.022). The study also identified potential barriers to accessing AI technology, such as lack of computer skills to navigate AI, lack of AI knowledge and awareness, and time constraints. The findings of this study provided valuable insights into the factors influencing student nurses' attitudes towards AI and shed light on their perceived barriers to accessing AI technology. By enhancing technological proficiency, increasing AI understanding, and providing practical experiences, nurse faculty can better prepare future nurses to effectively navigate the AI-driven healthcare environment and contribute to improved patient care outcomes. • Student nurses showed moderate readiness to adopt AI in their studies and a high perception of AI use in nursing practice. • Factors for AI readiness include technological proficiency, understanding of AI tools, and perceived AI use. • Barriers to accessing AI technology included lack of computer skills, AI knowledge and awareness, and time constraints.
(AN 171955005); ISSN: 02606917
CINAHL Complete

How robots and AI can support, not substitute, nursing practice: Artificial intelligence and robotics have the potential to aid nurses in aspects of clinical practice, from taking on repetitive tasks to speeding up decision-making.

Wed, 01 Nov 2023 04:00:00 +0000

Cancer Nursing Practice; 11/01/2023
We are hearing more about robots taking over the world, and the existential threat artificial intelligence (AI) could pose to humanity.
(AN 173311042); ISSN: 14754266
CINAHL Complete

Student nurses' attitudes, perceived utilization, and intention to adopt artificial intelligence (AI) technology in nursing practice: A cross-sectional study.

Wed, 01 Nov 2023 04:00:00 +0000

Nurse Education in Practice; 11/01/2023
The aim of this study was to investigate the attitudes and intentions of student nurses towards Artificial Intelligence (AI) in the context of nursing practice and to explore the relationship between their attitudes towards AI, their perceptions of AI utilization in nursing practice, and their intentions to adopt AI technology. The study hypothesized that perceived utilization of AI in nursing practice would positively influence the intention to use AI and that attitudes towards AI would mediate this relationship. AI has the potential to revolutionize various aspects of healthcare, including nursing practice. As AI technology continues to advance, it becomes increasingly important for nurse education to prepare student nurses to leverage AI technology and be willing to adopt it in their nursing practice. Cross-sectional design. A total of 200 student nurses from two government-owned nursing schools participated in the study. Mediation testing was performed using Hayes' PROCESS macro in SPSS (Model 4). Perceived AI utilization in nursing practice had a significant positive effect on student nurses' attitudes towards AI (β = 0.450, p < 0.001) and their intention to adopt AI technology (β = 0.458, p < 0.001). Attitudes towards AI partially mediated the relationship between perceived AI utilization in nursing practice and the intention to adopt AI technology (β = 0.255). The findings suggest that student nurses had favorable perceptions of AI utilization in nursing practice, expressed high intentions to adopt AI technology, and held positive attitudes towards AI. Furthermore, student nurses' perceptions of AI utilization in nursing practice influenced their attitudes towards AI, which, in turn, affected their intentions to adopt AI technology. Nursing education programs should incorporate AI-focused coursework, training, and experiential learning to further enhance students' readiness and proficiency in utilizing AI technology. Additionally, healthcare institutions should consider creating a supportive environment for nursing students to explore and embrace AI, ultimately preparing them for the evolving landscape of AI-enhanced healthcare practice. Student nurses' attitudes towards AI technology were influenced by their perceptions of AI utilization in nursing practice, which subsequently influenced their intentions to adopt AI technology.
(AN 173948032); ISSN: 14715953
CINAHL Complete

Artificial intelligence in nursing education 2: opportunities and threats.

Wed, 01 Nov 2023 04:00:00 +0000

Nursing Times; 11/01/2023
Artificial intelligence (AI) is being used to create new digital tools, such as the chatbot ChatGPT, which are starting to be used for teaching and learning in higher education. Nurse educators could use the opportunities offered by AI-based digital tools to enhance how they teach clinical knowledge and skills to students. Nursing students should learn to use AI tools appropriately, not just by understanding the opportunities they offer, but also by being aware of the threats they may pose to academic integrity, professional practice, and patient care. This second of two articles on AI in nursing education explores these opportunities and threats, and how to use generative AI in the context of nursing education.
(AN 173498181); ISSN: 09547762
CINAHL Complete

Application of machine learning and its effects on the development of a nursing guidance mobile app for sarcopenia.

Mon, 09 Oct 2023 04:00:00 +0000

BMC Nursing; 10/09/2023
Background: Aging leads to changes in the body system, such as sarcopenia. This can result in several health issues, particularly physical and mobility dysfunction. Asian people typically have little awareness of sarcopenia. Thus, this study incorporated nursing instruction into the mobile application design to allow users to easily learn about sarcopenia. Objective: This study evaluated a model for predicting high-risk populations for sarcopenia in home settings. We further developed a sarcopenia nursing guidance mobile application and assessed the effectiveness of this application in influencing sarcopenia-related knowledge and self-care awareness among participants. Methods: Using a one-group pretest–posttest design, data were collected from 120 participants at a teaching hospital in northern Taiwan. This study used an artificial intelligence algorithm to evaluate a model for predicting high-risk populations for sarcopenia. We developed and assessed the sarcopenia nursing guidance mobile application using a questionnaire based on the Mobile Application Rating Scale. Results: The application developed in this study enhanced participants' sarcopenia-related knowledge and awareness regarding self-care. After the three-month intervention, the knowledge and awareness was effectively increase, total score was from 4.15 ± 2.35 to 6.65 ± 0.85 and were significant for all questionnaire items (p values < 0.05). On average, 96.1% of the participants were satisfied with the mobile app. The artificial intelligence algorithm positively evaluated the home-use model for predicting high-risk sarcopenia groups. Conclusions: The mobile application of the sarcopenia nursing guidance for public use in home settings may help alleviate sarcopenia symptoms and reduce complications by enhancing individuals' self-care awareness and ability. Trial registration: NCT05363033, registered on 02/05/2022.
(AN 172866328); ISSN: 14726955
CINAHL Complete

Nursing education in the age of artificial intelligence powered Chatbots (AI-Chatbots): Are we ready yet?