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Background The health literacy among older adults deserves further investigation. This study aimed to conduct a multistage research to investigate the current status and key determinants of health literacy among Chinese older adults and evaluate the effectiveness of an active learning intervention in enhancing their health literacy. Methods In the first phase, a cross-sectional study surveyed 608 elderly residents. The second phase was a two-arm parallel randomized controlled trial (RCT) in which 120 older adults were randomly assigned to a three-month intervention ( n  = 60) or control group ( n  = 60). The active learning program intervention included health lectures, active discussions, heuristic questioning, and family homework, while the control group only received health literacy pamphlets. Health literacy scores were the primary outcome and were evaluated from five dimensions. The RCT data was collected at baseline and the completion of the intervention. Results In the cross-sectional study, the median (IQR) health literacy score was 4.355 (4.030, 4.647) (range: 0–5) Quantile regression showed that sex, education, number of children, self-reported health, chronic disease and insurance significantly affected health literacy. The intervention group showed significant improvement in all dimensions ( P  < 0.05), with significant group × time interactions in health knowledge, health behaviours, health skills, health intentions and total health literacy. Multiple linear regression indicated that marriage status related to health knowledge, education level related to health behaviours and total health literacy, chronic diseases and insurance factors related to health skills, and sex and insurance factors related to health intentions have significant effects. Conclusion The health literacy of older adults is influenced by individuals, families, and societal factors. The active learning program effectively enhances comprehensive health literacy and is a valuable strategy for advancing China’s proactive health strategy by mobilizing the roles of the individual, family, and society. Trial registration The trial has been retrospectively registered on April 8, 2025, at the Chinese Clinical Trial Registry (ChiCTR2500100396|| http://www.chictr.org.cn/ ), which is a primary registry of the International Clinical Trial Registry Platform of the World Health Organization.

PurposeThe concept of e-learning is essential in employee education since it provides different ways to develop employees' knowledge, skills and attitudes using modern technologies. E-learning has been overgrowing in employee education because learning can be held anytime and anywhere. In order to succeed in implementing e-learning and benefiting from its capacities, and avoiding potential threats in the country, it is necessary to address the factors affecting its success. This paper aims to test the role of internet of Things (IoT)-based systems, cloud-based services, virtual classes, evaluation tools, attitude, content management and creativity on the success of employees' e-learning programs based on a framework.Design/methodology/approachE-learning systems receive ever-increasing attention in academia, business and public administration. With the development of e-learning, employee education has also benefited from its capacities in various fields. To succeed in implementing e-learning and benefiting from its capacities, and avoiding potential threats in the country, it is necessary to address its success. The proposing of Information and Communications Technology (ICT)-based technologies such as the IoT, cloud, etc., in e-learning, can help transform education. Therefore, this paper aims to test the role of IoT-based systems, cloud-based services, virtual classes, evaluation tools, attitude, content management and creativity on the success of employees' e-learning programs based on a framework. The research model and the data collected from the questionnaires have been analyzed via Smart PLS 3.2. This study has utilized the SEM to evaluate the causal model's reliability and validity based on measurement. According to the literature in this study, a framework has been proposed that examines the impact of IoT-based systems, cloud-based services, virtual classes, evaluation tools, attitude, content management and creativity on employees' learning programs' success.FindingsThe results have shown that IoT-based systems, cloud-based services, virtual classes and evaluation tools are four significant factors affecting attitude, content management and creativity. The results have also shown that attitude, content management and creativity are three significant factors affecting employees' learning programs' success. The factors above are considered critical in explaining the success of employees' e-learning programs, but, as far as we know, there has been no study in which all these factors were demonstrated together.Practical implicationsFrom a practical viewpoint, the statistical outcomes support the important role of the following factors: IoT-based systems, cloud-based services, virtual classes, evaluation tools, attitude, content management and creativity. Henceforth, aspects relating to these factors got the attention of any organization to develop e-learning processes.Originality/valueThis research will contribute to the literature related to employees' e-learning programs' success by integrating all the mentioned variables. As far as we know, it is the first study to test these variables in Iran.

Harness the power of MATLAB to resolve a wide range of machine learning challenges. This book provides a series of examples of technologies critical to machine learning. Each example solves a real-world problem. All code in 'MATLAB Machine Learning Recipes' is executable. The toolbox that the code uses provides a complete set of functions needed to implement all aspects of machine learning. Authors Michael Paluszek and Stephanie Thomas show how all of these technologies allow the reader to build sophisticated applications to solve problems with pattern recognition, autonomous driving, expert systems, and much more.

ABSTRACTGovernmental COVID‐19 mandates in Ontario, Canada, resulted in a backlog of perioperative procedures. Organization leaders were required to expand services after the pandemic; however, the ongoing nursing shortage and college‐based structure of perioperative education programs complicated their response. In 2021, we developed an in‐house perioperative education program using a blended‐learning theory comprising online modules and videos, skills laboratory sessions, and clinical placement experiences. Nurses were required to apply for the program and remain employed at the facility for two years. Program evaluations showed that the novice nurses felt confident when beginning clinical experiences and preceptors believed the nurses were prepared for practice. Sixteen of 19 participants successfully completed the program, which helped resolve the staffing shortage. Novice nurses may benefit from a shadowing experience before applying for this type of program. Leaders in nonperioperative specialties should consider an in‐house education program to help meet staffing needs in their areas.

This book will introduce the AI algorithms to the beginners and will take on implementing AI tasks using various Java-based libraries. It will take a practical approach to get you up and running with building smarter applications using Java programming knowledge.

Purpose: Higher frequency of transcriptional errors in the radiotherapy electronic charts for patients on telecobalt was noted. We describe the impact of the quality improvement (QI) initiative under the department's incident learning program (ILP). Materials and Methods: The multidisciplinary quality team under ILP was formed to identify the root cause and introduce methods to reduce (smart goal) the current transcription error rate of 40% to <5% over 12 months. A root cause analysis including a fishbone diagram, Pareto chart, and action prioritization matrix was done to identify key drivers and interventions. Plan-Do-Study-Act (PDSA) Cycle strategy was undertaken. The primary outcome was percentage charts with transcriptional errors per month. The balancing measure was \"new errors\" due to interventions. All errors were identified and corrected before patient treatment. Results: The average baseline error rate was 44.14%. The two key drivers identified were education of the workforce involved and mechanical synchronization of various machine parameters. PDSA cycle 1 consisted of an education program and sensitization of the staff, post which the error rates dropped to 5.4% (t-test P = 0.03). Post-PDSA cycle 2 (synchronization of machine parameters), 1, 3, and 6 months and 1 year, the error rates were sustained to 5%, 4%, 3%, and 4% (t-test P > 0.05) with no new additional errors. Conclusions: With various generations of machines and technologies that are not synchronized, the proneness of transcription errors can be very high which can be identified and corrected with a typical QI process under ILP.

Fuelled by increasing computer power and algorithmic advances, machine learning techniques have become powerful tools for finding patterns in data. Quantum systems produce atypical patterns that classical systems are thought not to produce efficiently, so it is reasonable to postulate that quantum computers may outperform classical computers on machine learning tasks. The field of quantum machine learning explores how to devise and implement quantum software that could enable machine learning that is faster than that of classical computers. Recent work has produced quantum algorithms that could act as the building blocks of machine learning programs, but the hardware and software challenges are still considerable. Quantum machine learning software could enable quantum computers to learn complex patterns in data more efficiently than classical computers are able to.