12/28/2023 0 Comments Micro meso and macro levels![]() ![]() Attention should be placed on the importance of biopsychosocial perspective framing in the actual clinical and political context. To ensure good quality the PTs focused on the special needs of the patients, evidence-based fall prevention, interdisciplinary team work, good clinical competences, good skills in communication, and interpersonal relations. Our findings indicate that the PTs’ role reflects their abilities to change and improve their professional work in accordance with evidence based knowledge. Success in the role of physiotherapists in fall prevention depends on the empowering leadership and working culture, as well as on the time and multifaceted professional competence of the clinicians. The three themes were as follows: 1) always moving and changing: the competent explorative knowledge-hungry clinician’s multifaceted role 2) multiprofessional – but in the end alone 3) reaching out – from the bottom to the top. A key factor for this role is to take an integrative biopsychosocial approach based on how biological and psychosocial factors are uniquely related in fall prevention. The core theme was ‘capability to cope with the tensions between the micro-, meso- and macro-levels in fall, prevention’, which indicated the importance of an evolving multifaceted, evidence based and innovative physiotherapy role. The analysis resulted in a core theme and three subthemes. Data were analysed using a qualitative thematic analysis. Semi-structured interviews were conducted with 17 physiotherapists. Therefore, the purpose of the present study is to explore physical therapists’ (PTs) view of how they experience and perceive their role working with fall prevention in a community care setting. Physiotherapists are a key resource in this context, but there is sparse knowledge about how they perceive their role in the primary care setting. Falls are a global public health concern. Experimental results demonstrate that the searched BurgerFormer architectures achieve comparable even superior performance compared with current state-of-the-art Transformers on the ImageNet and COCO datasets. Meanwhile, we propose a hybrid sampling method for effectively training the supernet. At the macroscopic level, we search for the depth, width, and expansion ratio of the network based on the multi-stage architecture. At the mesoscopic level, a hamburger structure is searched out as the basic BurgerFormer block. At the microscopic level, we enrich the atomic operations to include various normalizations, activation functions, and basic operations (e.g., multi-head self attention, average pooling). Micro, meso, and macro correspond to the granularity levels of operation, block and stage, respectively. By revisiting typical search spaces, we design micro-meso-macro space to search for Transformer-like architectures, namely BurgerFormer. Recently, MetaFormer found that simple average pooling can achieve impressive performance, which naturally raises the question of how to design a search space to search diverse and high-performance Transformer-like architectures. %X With the success of Transformers in the computer vision field, the automated design of vision Transformers has attracted significant attention. %C Proceedings of Machine Learning Research %B Proceedings of the 39th International Conference on Machine Learning %T Searching for BurgerFormer with Micro-Meso-Macro Space Design ![]() With the success of Transformers in the computer vision field, the automated design of vision Transformers has attracted significant attention. ![]()
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