Aim: This study aimed to determine the attitude of Australian nursing lecturers to the use of technology applied to the teaching and learning of nursing students.
Background: The use of technology in teaching was accelerated in reaction to the COVID-19 restrictions whereby measures, including social distancing and lockdowns, forced many higher education courses to transition online. Lecturers play a key role in the integration of technology in teaching, as it is the lecturer, not the technology, who facilitates the students' learning experience.
Design: A cross sectional survey design was used for this study, distributed from April to June of 2022. The purpose of the survey was to gather technology readiness data (via the TRI 2 questions) and descriptive data representative of the nursing academic population in Australia.
Results: There was no statistically significant differences between participants based on demographic data (such as gender or age). There was an association between TRI 2 score and: the sum of elearning technologies engaged with; the frequency of engagement with technology and self-rated confidence with elearning. Of note were statistically significant differences of TRI between states/territories. Finally, there was an inverse relationship between the impact of COVID-19 restrictions and TRI 2 score.
Conclusion: The study found that there was significant variation between states/territories and self-reported impact of TRI. Given that increased frequency and increased number of technologies engaged with are associated with technology readiness the variation between states/territories lockdowns which required engagement with technology, may have had an impact on the nursing academics attitude to technology. Importantly, this study found those who were highly technology ready found COVID-19 restrictions had less impact on them, suggesting that technology readiness may have assisted their transition to online learning.
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http://dx.doi.org/10.1016/j.nepr.2023.103719 | DOI Listing |
Sci Data
January 2025
Luxembourg Institute of Science and Technology, Esch-sur-Alzette, Luxembourg.
To ensure the fairness and trustworthiness of machine learning (ML) systems, recent legislative initiatives and relevant research in the ML community have pointed out the need to document the data used to train ML models. Besides, data-sharing practices in many scientific domains have evolved in recent years for reproducibility purposes. In this sense, academic institutions' adoption of these practices has encouraged researchers to publish their data and technical documentation in peer-reviewed publications such as data papers.
View Article and Find Full Text PDFJMIR Rehabil Assist Technol
January 2025
Department of Health and Nursing Science, Faculty of Health and Sport Sciences, University of Agder, Kristiansand, Norway.
Background: Health care is shifting toward 5 proactive approaches: personalized, participatory, preventive, predictive, and precision-focused services (P5 medicine). This patient-centered care leverages technologies such as artificial intelligence (AI)-powered robots, which can personalize and enhance services for users with disabilities. These advancements are crucial given the World Health Organization's projection of a global shortage of up to 10 million health care workers by 2030.
View Article and Find Full Text PDFCurr Psychiatry Rep
January 2025
Center for Military Medicine Research, University of Pittsburgh, Pittsburgh, PA, USA.
Purpose Of Review: Medicine and specifically mental health have been affected by emerging technologies advancing mental health treatment while at the same time bringing new challenges and stressors to the battlefield, military systems, and the warfighter.
Recent Findings: This article reviews the evolving positive and negative impacts of technology on combat mental health and treatment. A history of technology and military mental health concerns and services is followed by an overview of present benefits and risks.
Mayo Clin Proc Digit Health
December 2024
Department Radiology, Stanford University, Stanford, CA.
Artificial intelligence (AI) and machine learning (ML) are driving innovation in biosciences and are already affecting key elements of medical scholarship and clinical care. Many schools of medicine are capitalizing on the promise of these new technologies by establishing academic units to catalyze and grow research and innovation in AI/ML. At Stanford University, we have developed a successful model for an AI/ML research center with support from academic leaders, clinical departments, extramural grants, and industry partners.
View Article and Find Full Text PDFJ Food Sci
January 2025
College of Engineering, Jiangxi Agricultural University, Nanchang, China.
In the intelligent harvesting of eggplant, the lack of in situ identification technology makes it challenging to determine the maturity of purple eggplant fruit. The length of the fruit-setting date can determine when the eggplant is ready to be harvested. This study uses deep learning techniques to predict the date of fruit maturity.
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