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Objective To detect and characterize sleep quality profiles and to analyze their relationship with depression, anxiety, and stress in a sample of 1,861 Chilean students. Materials and Methods After providing informed consent, the students filled out online questionnaires and received immediate feedback. Hierarchical cluster analyses were conducted to detect sleep quality profiles, which were characterized using the Kruskal-Wallis's test. The Pearson correlation coefficient was used to correlate sleep quality profiles with mental health variables. The dendrogram revealed four distinct groups of interest, each with different patterns in the subscales of the Pittsburgh Sleep Quality Index (PSQI). Results The results enabled us to establish four sleep quality profiles based on hierarchical cluster analysis, which were, in different ways, associated with the prevalence of symptoms of mental health issues. A profile of good sleeper was found, which presents good overall sleep quality and mild symptoms of mental health issues. The effective sleeper profile presents poor subjective sleep quality and good sleep efficiency, with mild symptoms of mental health issues. The poor sleeper profile presents poor overall sleep quality, sleeping between 5 and 6 hours and presenting moderate symptoms of depression, anxiety, and stress. The sleeper with hypnotic use profile obtains the most deficient results in sleep quality and presents symptoms of severe mental health issues. Conclusions The present study revealed a strong association and correlation between sleep quality profiles and mental health issues. Four distinct sleep quality profiles were identified, showing notable differences. This understanding enables the application of targeted preventive strategies according to each profile.
Objective To detect and characterize sleep quality profiles and to analyze their relationship with depression, anxiety, and stress in a sample of 1,861 Chilean students. Materials and Methods After providing informed consent, the students filled out online questionnaires and received immediate feedback. Hierarchical cluster analyses were conducted to detect sleep quality profiles, which were characterized using the Kruskal-Wallis's test. The Pearson correlation coefficient was used to correlate sleep quality profiles with mental health variables. The dendrogram revealed four distinct groups of interest, each with different patterns in the subscales of the Pittsburgh Sleep Quality Index (PSQI). Results The results enabled us to establish four sleep quality profiles based on hierarchical cluster analysis, which were, in different ways, associated with the prevalence of symptoms of mental health issues. A profile of good sleeper was found, which presents good overall sleep quality and mild symptoms of mental health issues. The effective sleeper profile presents poor subjective sleep quality and good sleep efficiency, with mild symptoms of mental health issues. The poor sleeper profile presents poor overall sleep quality, sleeping between 5 and 6 hours and presenting moderate symptoms of depression, anxiety, and stress. The sleeper with hypnotic use profile obtains the most deficient results in sleep quality and presents symptoms of severe mental health issues. Conclusions The present study revealed a strong association and correlation between sleep quality profiles and mental health issues. Four distinct sleep quality profiles were identified, showing notable differences. This understanding enables the application of targeted preventive strategies according to each profile.
Se realizó un análisis bibliométrico de la literatura sobre inteligencia artificial en el campo de los negocios, se utilizó la base de datos Scopus como fuente principal. El estudio identificó un aumento significativo en las publicaciones académicas en los últimos años, lo cual evidenció el creciente interés en cómo la inteligencia artificial transformó el panorama empresarial. Se analizaron las principales tendencias, destaca entre estas la adopción de técnicas de aprendizaje automático para mejorar la eficiencia operativa y la experiencia del cliente, así como el uso de análisis predictivo para apoyar la toma de decisiones basadas en datos. Además, se exploraron las barreras específicas que enfrentaron las empresas en Latinoamérica para la implementación de la inteligencia artificial. Se incluyeron limitaciones tecnológicas, escasez de talento especializado, restricciones financieras y desafíos regulatorios. A través del análisis cualitativo de artículos relevantes, se identificaron las principales líneas de investigación orientadas a superar estas barreras, tales como el desarrollo de infraestructura tecnológica, programas de capacitación y marcos éticos y legales claros. Se concluyó que, aunque existen obstáculos significativos, hay oportunidades para impulsar la integración de la inteligencia artificial en los negocios de la región, lo cual podría contribuir al desarrollo económico y competitividad en el mercado global.
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