Health sciences lifelong learning and professional development in the era of artificial intelligence.

Introduction: In the field of healthcare, lifelong learning is considered a quality indicator due to its potential to enhance healthcare services and the knowledge and skills of healthcare professionals. The purpose of this research is to investigate and document the attitudes and behavior of healthcare professionals from different healthcare institutions toward lifelong learning using artificial intelligence.

The Study Achieves Two Objectives: Identifying healthcare professionals' interest in formal lifelong learning and determining how they engage in lifelong learning.

The characterization of a hospitalized population at the pediatric emergency service of Mother and Child Hospital, Marrakech, Morocco.

Introduction: the pediatric emergency department is the first contact between the population and the hospital. Consequently, its dysfunction influences the quality of the general health care system. However, any successful policy must first be based on convincing results hence the need to better explore this service, diagnose the various dysfunctions, and survey disease trends to identify the needs of the local population.

High-Fidelity Simulation Effects on Cardiopulmonary Resuscitation Self-Efficacy and Knowledge Retention in Undergraduate Nursing Students: A Two-Group, Experimental, Longitudinal Pilot Study.

This pilot study aimed to assess the effects of high-fidelity simulation on cardiopulmonary resuscitation (CPR) self-efficacy and knowledge retention compared to case-based learning. A two-group, experimental, longitudinal design was adopted. Fifty-two undergraduate nursing students were invited to participate in the study, which was conducted between March and May 2019.

Modelization of Covid-19 pandemic spreading: A machine learning forecasting with relaxation scenarios of countermeasures.

Background & Objective: Mathematical modeling is the most scientific technique to understand the evolution of natural phenomena, including the spread of infectious diseases. Therefore, these modeling tools have been widely used in epidemiology for predicting risks and decision-making processes. The purpose of this paper is to provide an effective mathematical model for predicting the spread of Covid-19 pandemic.