DETERMINANTS DE LA COLLABORATION INTERPROFESSIONNELLE EN INTRA-HOSPITALIER PENDANT LA PANDEMIE COVID-19 : CAS CHP ALHAOUZ

Résumé : La collaboration interprofessionnelle (CIP) est un outil managérial très utilisé dans le domaine de la santé. Ce travailest une étude qualitative exploratoire qui a pour objectif de décrire la CIP, ses déterminants, et les difficultés rencontrées lors de son application dans la gestion de la pandémie Covid-19 dans le Centre Hospitalier provincial Alhaouz. A travers un codage manuel sémantique de 19 entretienssemi-directif ; la CIP a été définie comme un travail d’équipe obligatoire permettant un maximum de partage de connaissances et une complémentarité des disciplines pour une meilleure prise en charge des patients et la lutte contre la propagation du virus. 5 thèmes et 8 déterminants de la CIP résultent dont 4 individuels, 3 organisationnels et 1 seul exogène représenté par le risque lié à l’exposition au virus. L’émotion est le premier déterminant qui régule la CIP, suivie des compétences du leadership. Les contraintes confrontées par les professionnels de santé en collaborant sont essentiellement liées au degré du développement personnel en matière de communication, et de capacité de s’adapter au changement. En conclusion ; Les professionnels de la santé recommandent l’adoption de la collaboration interprofessionnelle comme outil de gestion dans l’exercice quotidien  et le recours au leadership collaboratif pour assurer le management du changement du système de santé. Mots clés : Collaboration interprofessionnelle, Pandémie Covid-19, management du changement. Classification JEL: I, I1, I18 Abstract Interprofessional collaboration (IPC) is a managerial tool used in several fields, including health. This work is an exploratory qualitative study which aims to describe the CIP, its determinants, and the difficulties encountered during the application of this tool in the management of the health crisis in the Alhaouz Provincial Hospital Center. Through a semantic manual coding of 19 semi-structured interviews; CIP has been defined as mandatory teamwork allowing maximum sharing of knowledge and complementarity of disciplines for better patient care and the fight against the spread of the virus. 5 themes and 8 determinants of the IPC result, including 4 individual, 3 organizational and only 1 exogenous represented by the risk linked to exposure to the virus. Emotion is the first determinant that regulates IPC, followed by leadership skills. The constraints faced by health professionals when collaborating are essentially related to the degree of personal development in terms of communication, continuous training and the ability to adapt to change. In conclusion; Health professionals recommend adopting interprofessional collaboration as a management tool in daily practice for better patient care, and to establish collaborative leadership capable of ensuring the management of change in the health system. Keywords: Interprofessional collaboration, Covid-19 pandemic, change management. JEL Rating: I, I1, I18 &nbsp

FCOSH: A novel single-head FCOS for faster object detection in autonomous-driving systems

In autonomous driving systems, object detection plays a pivotal role by facilitating their ability to perceive the surrounding road environment effectively. Object detection's foremost challenge pertains to its real-time operational capabilities. Achieving this necessitates reducing the detectors' computational complexity while preserving their accuracy. Nevertheless, most of the approach in object detection involves dividing image processing over multiple heads, each tasked with detecting objects at particular scales. Even though this approach improves detection accuracy, it adds an extra computational burden. In this study, our objective is to assess the feasibility of employing a single head within the originally multi-headed architecture of the FCOS detector. In response to the challenges posed by this significant modification, we propose a set of straightforward solutions, resulting in the development of a novel Fully Convolutional One-Stage with a Single Head (FCOSH) detector. Through experiments on the BDD100K benchmark, our FCOSH detector exhibits substantial improvements in computational efficiency relative to the original FCOS while concurrently achieving a superior detection 0.5% accuracy. Specifically, FCOSH achieves an 18% reduction in inference time, a 24% reduction in required FLOPs, and a 10% decrease in the number of model parameters compared to FCOS

Automatic detection of stereotyped movements in autistic children using the Kinect sensor

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FCOSH: A novel single-head FCOS for faster object detection in autonomous-driving systems

In autonomous driving systems, object detection plays a pivotal role by facilitating their ability to perceive the surrounding road environment effectively. Object detection’s foremost challenge pertains to its real-time operational capabilities. Achieving this necessitates reducing the detectors’ computational complexity while preserving their accuracy. Nevertheless, most of the approach in object detection involves dividing image processing over multiple heads, each tasked with detecting objects at particular scales. Even though this approach improves detection accuracy, it adds an extra computational burden. In this study, our objective is to assess the feasibility of employing a single head within the originally multi-headed architecture of the FCOS detector. In response to the challenges posed by this significant modification, we propose a set of straightforward solutions, resulting in the development of a novel Fully Convolutional One-Stage with a Single Head (FCOSH) detector. Through experiments on the BDD100K benchmark, our FCOSH detector exhibits substantial improvements in computational efficiency relative to the original FCOS while concurrently achieving a superior detection 0.5% accuracy. Specifically, FCOSH achieves an 18% reduction in inference time, a 24% reduction in required FLOPs, and a 10% decrease in the number of model parameters compared to FCOS.</p