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Health professionals’ competencies in the framework of complexity: Digital training model for education 4.0
(Editorial CSIC, 2024-06-12) Fernández Luque, Antonia María; Ramírez Montoya, María Soledad; https://ror.org/01mqsmm97; https://ror.org/03ayjn504
Complex environments require changes in training models for health professionals to support access to knowledge for teaching and clinical research decision-making. These changes require digital competency training to successfully address knowledge acquisition and lifelong learning in the digital health ecosystem. This research aims to analyse the habits of use of information resources by healthcare professionals in professional practices and to propose a model for training in digital competence. An exploratory and explanatory research method was chosen. Data collection techniques included pre-test and post-test questionnaires for the training activities. The program designed to develop digital competency under the model proposed in this study had significant acceptance among users who valued all the contents, resources, and teachers and considered it effective. Training in digital competencies is one of the drivers of digital transformation. Teaching-learning models in virtual environments pose challenges for 21st-century libraries.
Demographic and school factors associated with digital competences in higher education students
(Bastas, 2024-02-26) Glasserman Morales, Leonardo David; Alcántar Nieblas, Carolina; Sisto, Marcela Ines; Tecnologico de Monterrey; Universidad Católica de Argentina; https://ror.org/03ayjn504; https://ror.org/0422kzb24
Nowadays, digital competencies encompass skills and attitudes with technical, informational, content, media, and communication aspects that are crucial for students and future professionals. Hence, there is a need to investigate the possible correlations between demographic and contextual variables and the development of digital competencies in higher education. This paper reports on several university-student demographic factors associated with digital competencies. The work used a quantitative approach with descriptive statistical techniques such as a means test and Pearson correlation analysis. The findings identified that (a) there are statistically significant differences between the mean obtained in the previous semester in digital competencies and the gender of the students, (b) there are no statistically significant differences in the final mean for digital competencies and the students’ institution of origin, and (c) the variables included in the study are statistically significant. They also found that the mean attained by the university students in the previous semester had a strong predictive power of student performance; in contrast, the student’s high school institution of origin variable was a weak predictor of their digital competency. This paper presents the findings and implications for practice and research.

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