Documentos donde el Tema es "Materias > Ciencias Sociales"
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Artículo
Materias > Ciencias Sociales
Universidad Europea del Atlántico > Investigación > Artículos y libros
Fundación Universitaria Internacional de Colombia > Investigación > Artículos y libros
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Artículos y libros
Universidad de La Romana > Investigación > Producción Científica Abierto Inglés To improve organisational performance, it is crucial to cultivate an environment and culture that, through shared values, attitudes, behaviours, and sentiments, enables all employees to feel comfortable in performing their work. This represents a recognised gap within the current Cuban business context. Drawing from identified challenges and the introduction of a values-based coaching programme at the state-owned company GEDEME to address this gap, the aim of this study is to evaluate the impact of the values-based coaching programme (CpV) on organisational culture among both tactical and strategic employees within GEDEME. The research adopts a mixed-methods design. On one hand, the non-parametric McNemar test was utilised to assess before-and-after differences, while a case-study approach facilitated the exploration of specific questions, such as identifying the values actually practised beyond those outlined in the formal business plan and understanding the extent and nature of value shifts following the implementation of the coaching programme. The results confirmed the primary hypothesis: the values-based coaching programme at GEDEME had a positive effect on employees' perceptions of organisational culture, resulting in a substantial increase in the number of values both practised and perceived by its members. Caro Montero, Elizabeth; Soriano Flores, Emmanuel; Silva Alvarado, Eduardo René y Garat de Marin, Mirtha Silvana elizabeth.caro@uneatlantico.es, emmanuel.soriano@uneatlantico.es, eduardo.silva@funiber.org, silvana.marin@uneatlantico.es
Fundación Universitaria Internacional de Colombia > Investigación > Artículos y libros
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Artículos y libros
Universidad de La Romana > Investigación > Producción Científica Abierto Inglés To improve organisational performance, it is crucial to cultivate an environment and culture that, through shared values, attitudes, behaviours, and sentiments, enables all employees to feel comfortable in performing their work. This represents a recognised gap within the current Cuban business context. Drawing from identified challenges and the introduction of a values-based coaching programme at the state-owned company GEDEME to address this gap, the aim of this study is to evaluate the impact of the values-based coaching programme (CpV) on organisational culture among both tactical and strategic employees within GEDEME. The research adopts a mixed-methods design. On one hand, the non-parametric McNemar test was utilised to assess before-and-after differences, while a case-study approach facilitated the exploration of specific questions, such as identifying the values actually practised beyond those outlined in the formal business plan and understanding the extent and nature of value shifts following the implementation of the coaching programme. The results confirmed the primary hypothesis: the values-based coaching programme at GEDEME had a positive effect on employees' perceptions of organisational culture, resulting in a substantial increase in the number of values both practised and perceived by its members. Caro Montero, Elizabeth; Soriano Flores, Emmanuel; Silva Alvarado, Eduardo René y Garat de Marin, Mirtha Silvana elizabeth.caro@uneatlantico.es, emmanuel.soriano@uneatlantico.es, eduardo.silva@funiber.org, silvana.marin@uneatlantico.es
Organizational Culture Assessment Based on a Values-Based Coaching Program for Strategic Level Employees: The Case of GEDEME, Cuba.
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Artículo
Materias > Biomedicina
Materias > Ciencias Sociales
Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Artículos y libros
Fundación Universitaria Internacional de Colombia > Investigación > Artículos y libros
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Artículos y libros
Universidad de La Romana > Investigación > Producción Científica Abierto Inglés Aim: The development of predictive models for patients treated by emergency medical services (EMS) is on the rise in the emergency field. However, how these models evolve over time has not been studied. The objective of the present work is to compare the characteristics of patients who present mortality in the short, medium and long term, and to derive and validate a predictive model for each mortality time. Methods: A prospective multicenter study was conducted, which included adult patients with unselected acute illness who were treated by EMS. The primary outcome was noncumulative mortality from all causes by time windows including 30-day mortality, 31- to 180-day mortality, and 181- to 365-day mortality. Prehospital predictors included demographic variables, standard vital signs, prehospital laboratory tests, and comorbidities. Results: A total of 4830 patients were enrolled. The noncumulative mortalities at 30, 180, and 365 days were 10.8%, 6.6%, and 3.5%, respectively. The best predictive value was shown for 30-day mortality (AUC = 0.930; 95% CI: 0.919–0.940), followed by 180-day (AUC = 0.852; 95% CI: 0.832–0.871) and 365-day (AUC = 0.806; 95% CI: 0.778–0.833) mortality. Discussion: Rapid characterization of patients at risk of short-, medium-, or long-term mortality could help EMS to improve the treatment of patients suffering from acute illnesses. Enriquez de Salamanca Gambara, Rodrigo; Sanz-García, Ancor; del Pozo Vegas, Carlos; López-Izquierdo, Raúl; Sánchez Soberón, Irene; Delgado Benito, Juan F.; Martínez Díaz, Raquel; Mazas Pérez-Oleaga, Cristina; Martínez López, Nohora Milena; Dominguez Azpíroz, Irma y Martín-Rodríguez, Francisco SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, raquel.martinez@uneatlantico.es, cristina.mazas@uneatlantico.es, nohora.martinez@uneatlantico.es, irma.dominguez@unini.edu.mx, SIN ESPECIFICAR
Materias > Ciencias Sociales
Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Artículos y libros
Fundación Universitaria Internacional de Colombia > Investigación > Artículos y libros
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Artículos y libros
Universidad de La Romana > Investigación > Producción Científica Abierto Inglés Aim: The development of predictive models for patients treated by emergency medical services (EMS) is on the rise in the emergency field. However, how these models evolve over time has not been studied. The objective of the present work is to compare the characteristics of patients who present mortality in the short, medium and long term, and to derive and validate a predictive model for each mortality time. Methods: A prospective multicenter study was conducted, which included adult patients with unselected acute illness who were treated by EMS. The primary outcome was noncumulative mortality from all causes by time windows including 30-day mortality, 31- to 180-day mortality, and 181- to 365-day mortality. Prehospital predictors included demographic variables, standard vital signs, prehospital laboratory tests, and comorbidities. Results: A total of 4830 patients were enrolled. The noncumulative mortalities at 30, 180, and 365 days were 10.8%, 6.6%, and 3.5%, respectively. The best predictive value was shown for 30-day mortality (AUC = 0.930; 95% CI: 0.919–0.940), followed by 180-day (AUC = 0.852; 95% CI: 0.832–0.871) and 365-day (AUC = 0.806; 95% CI: 0.778–0.833) mortality. Discussion: Rapid characterization of patients at risk of short-, medium-, or long-term mortality could help EMS to improve the treatment of patients suffering from acute illnesses. Enriquez de Salamanca Gambara, Rodrigo; Sanz-García, Ancor; del Pozo Vegas, Carlos; López-Izquierdo, Raúl; Sánchez Soberón, Irene; Delgado Benito, Juan F.; Martínez Díaz, Raquel; Mazas Pérez-Oleaga, Cristina; Martínez López, Nohora Milena; Dominguez Azpíroz, Irma y Martín-Rodríguez, Francisco SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, raquel.martinez@uneatlantico.es, cristina.mazas@uneatlantico.es, nohora.martinez@uneatlantico.es, irma.dominguez@unini.edu.mx, SIN ESPECIFICAR
A Comparison of the Clinical Characteristics of Short-, Mid-, and Long-Term Mortality in Patients Attended by the Emergency Medical Services: An Observational Study.
S
Artículo
Materias > Ciencias Sociales
Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Artículos y libros
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Artículos y libros
Universidad de La Romana > Investigación > Producción Científica Cerrado Inglés The global economies depend heavily on commodity prices, which have an effect on businesses, investors, and consumers worldwide. It is important to be able to estimate commodity prices accurately because it facilitates risk management, distribution of resources, and intelligent decision-making. For the current status of research in this area, this study gives a systematic review of the available commodity price prediction models specifically for house prices in real estate. We determine and compare different commodity price prediction models and techniques used in academic and commercial settings. The paper is divided into three primary categories: house price prediction, stock price prediction, and natural gas price prediction. Within each category, various methodologies are utilized, including ensemble methods, neural networks, support vector machines, time series analysis, and regression analysis. This review offers a thorough analysis of both the strengths and limitations of current models, as well as the major variables affecting their performance. Additionally, potential challenges associated with models are discussed, and insights are provided for addressing different prediction issues. The review serves as an invaluable guide for researchers, practitioners, and policymakers seeking to gain deeper knowledge of the latest advancements in commodity price prediction. The findings indicate that machine learning holds significant potential for optimizing house price predictions. Saeed, Natasha; Shafi, Imran; Pervez, Sidra; Bautista Thompson, Ernesto; Kuc Castilla, Ángel Gabriel; Samad, Md Abdus y Ashraf, Imran SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, ernesto.bautista@unini.edu.mx, angel.kuc@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR
Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Artículos y libros
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Artículos y libros
Universidad de La Romana > Investigación > Producción Científica Cerrado Inglés The global economies depend heavily on commodity prices, which have an effect on businesses, investors, and consumers worldwide. It is important to be able to estimate commodity prices accurately because it facilitates risk management, distribution of resources, and intelligent decision-making. For the current status of research in this area, this study gives a systematic review of the available commodity price prediction models specifically for house prices in real estate. We determine and compare different commodity price prediction models and techniques used in academic and commercial settings. The paper is divided into three primary categories: house price prediction, stock price prediction, and natural gas price prediction. Within each category, various methodologies are utilized, including ensemble methods, neural networks, support vector machines, time series analysis, and regression analysis. This review offers a thorough analysis of both the strengths and limitations of current models, as well as the major variables affecting their performance. Additionally, potential challenges associated with models are discussed, and insights are provided for addressing different prediction issues. The review serves as an invaluable guide for researchers, practitioners, and policymakers seeking to gain deeper knowledge of the latest advancements in commodity price prediction. The findings indicate that machine learning holds significant potential for optimizing house price predictions. Saeed, Natasha; Shafi, Imran; Pervez, Sidra; Bautista Thompson, Ernesto; Kuc Castilla, Ángel Gabriel; Samad, Md Abdus y Ashraf, Imran SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, ernesto.bautista@unini.edu.mx, angel.kuc@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR
Intelligent Decision Making for Commodities Price Prediction: Opportunities, Challenges and Future Avenues.
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