Accessible Resistance Movement Experiences for Elementary Students and Educators

What is meant by accessible resistance movement and why is the elementary education phase proposed as such a superb period in a child’s life to gain competence and knowledge using resistance activity? This chapter presents a case and a means to do just that. The resistance program is called ‘I Can Resist’. It is shared with accompanying pedagogical methods to scaffold learning and progress motor competence and biomotor fitness (agility that improves health through skill-related fitness). Interleaved through the progressions are ways to increase the self-management in how to participate in and create meaningful ways to improve targeted benefits. ‘I Can Resist’ is designed for novices to more experienced, participants and tutors alike. It was developed primarily for physical education supporting national curricular policy and implementation as regards knowledge and fitness outcomes. It was extended beyond the curriculum expectation in order to encourage greater use of the available affordances beyond the curriculum for lifelong health and well-being. Current findings are examined and insights offered. The ‘I Can Resist’ program is underpinned through research and theoretical application. It is showcased as interwoven with the means to develop agentic thinking and action. This ecological approach to and through resistance movement is contextually adaptable

Acute stroke CDS: automatic retrieval of thrombolysis contraindications from unstructured clinical letters

Introduction: Thrombolysis treatment for acute ischaemic stroke can lead to better outcomes if administered early enough. However, contraindications exist which put the patient at greater risk of a bleed (e.g. recent major surgery, anticoagulant medication). Therefore, clinicians must check a patient's past medical history before proceeding with treatment. In this work we present a machine learning approach for accurate automatic detection of this information in unstructured text documents such as discharge letters or referral letters, to support the clinician in making a decision about whether to administer thrombolysis. Methods: We consulted local and national guidelines for thrombolysis eligibility, identifying 86 entities which are relevant to the thrombolysis decision. A total of 8,067 documents from 2,912 patients were manually annotated with these entities by medical students and clinicians. Using this data, we trained and validated several transformer-based named entity recognition (NER) models, focusing on transformer models which have been pre-trained on a biomedical corpus as these have shown most promise in the biomedical NER literature. Results: Our best model was a PubMedBERT-based approach, which obtained a lenient micro/macro F1 score of 0.829/0.723. Ensembling 5 variants of this model gave a significant boost to precision, obtaining micro/macro F1 of 0.846/0.734 which approaches the human annotator performance of 0.847/0.839. We further propose numeric definitions for the concepts of name regularity (similarity of all spans which refer to an entity) and context regularity (similarity of all context surrounding mentions of an entity), using these to analyse the types of errors made by the system and finding that the name regularity of an entity is a stronger predictor of model performance than raw training set frequency. Discussion: Overall, this work shows the potential of machine learning to provide clinical decision support (CDS) for the time-critical decision of thrombolysis administration in ischaemic stroke by quickly surfacing relevant information, leading to prompt treatment and hence to better patient outcomes