Psychosocial Environment of Science Laboratory Classrooms in Canadian Schools and Universities

The Science Laboratory Environment Inventory assesses students’ or teachers’ perceptions of five dimensions of actual or preferred classroom environment, namely, Student Cohesiveness, Open-Endedness, Integration, Rule Clarity, and Material Environment. The instrument was field-tested in Canada, Australia, the United States, England, Israel, and Nigeria, both in secondary and in post- secondary institutions. Various analyses attested to each scale’s internal consist- ency, reliability, discriminant validity, factorial validity, predictive validity, and ability to differentiate between the perceptions of students in different classes. The instrument is equally valid for use in its actual and preferred versions, for senior secondary school and university laboratory classes, for the individual or the class mean as the unit of analysis, and for each of the six countries. Le Science Laboratory Environment Inventory évalue les perceptions des étu- diants ou des enseignants ayant trait à cinq facettes de la salle de cours, à savoir la cohésion des étudiants, l’ouverture, l’intégration, la clarté du règlement et l’environnement physique. L’instrument a été utilisé au secondaire et au post- secondaire au Canada, en Australie, aux États-Unis, en Angleterre, en Israël et au Nigeria. Diverses analyses attestent la cohérence interne, la fidélité, la validité discriminante, la validité factorielle et la validité prédictive de chaque échelle comme son pouvoir de différencier les perceptions des étudiants dans diverses classes. La validité de l’instrument a également été établie dans ses versions présente et préférée, pour une utilisation dans les dernières années du secondaire et les cours de laboratoire à l’université, pour la moyenne par individu ou par classe comme unité d’analyse et pour chacun de ses six pays.

Making a Difference to the Student Experience Through Purposeful Course Design

The study described examines the student experience in a graduate inclusive education course purposefully designed to address areas of need identified. These include the need for theory to underpin course design, the need for collaborative practice, and the need to reduce the theory-to-practice gap. Throughout their enrolment and after course completion, feedback from students is presented and examined in order to determine whether these needs have been met through the design of the course. Findings suggest that the organisation of learning materials, embedded capacity-building skills, and the practical applicability of course content was highly regarded by students and contributed to their learning. Additionally, the design process used addressed the areas of need identified in an authentic way

Artificial intelligence in cardiology: the debate continues

In 1955, when John McCarthy and his colleagues proposed their first study of artificial intelligence, they suggested that ‘every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it’. Whether that might ever be possible would depend on how we define intelligence, but what is indisputable is that new methods are needed to analyse and interpret the copious information provided by digital medical images, genomic databases, and biobanks. Technological advances have enabled applications of artificial intelligence (AI) including machine learning (ML) to be implemented into clinical practice, and their related scientific literature is exploding. Advocates argue enthusiastically that AI will transform many aspects of clinical cardiovascular medicine, while sceptics stress the importance of caution and the need for more evidence. This report summarizes the main opposing arguments that were presented in a debate at the 2021 Congress of the European Society of Cardiology. Artificial intelligence is an advanced analytical technique that should be considered when conventional statistical methods are insufficient, but testing a hypothesis or solving a clinical problem—not finding another application for AI—remains the most important objective. Artificial intelligence and ML methods should be transparent and interpretable, if they are to be approved by regulators and trusted to provide support for clinical decisions. Physicians need to understand AI methods and collaborate with engineers. Few applications have yet been shown to have a positive impact on clinical outcomes, so investment in research is essential

Review of the quarter's economic trends [July 1976]

The initial recovery phase of the leading economies in the second half of 1975 occurred at a fairly leisurely pace compared to past experience of the upturn. However, the first quarter of 1976 has seen industrial production expanding rapidly in most countries, with an expected rise of 10% over the same period last year. This increases momentum in the world economy has led to revision of official forecasts of many important indicators. The volume of world trade is now expected to rise by 10% this year. Total output, which fell by 1 ½% last year, should increase by 5% this year. Inflation in most countries is now in single figures on an annual rate basis. Profit levels have improved considerably: so far there is little evidence of a recovery in investment in plant and machinery