Lectures related to Assessing Intervention Characteristics

Introduces key objectives and principles of Implementation Research for implementers.

Discusses risk governance, decision-making, stakeholder engagement, and risk assessment.

Covers tools for analyzing implementation problems and stakeholder engagement in solving them.

Explores the transition of genomic medicine from research to clinic, emphasizing personalized medicine and ethical considerations.

Highlights the role of implementers in facilitating the uptake of evidence-based interventions.

Explores evidence-based strategies for preventing sports injuries in alpine ski racing.

Emphasizes the importance of evidence-informed teaching methods in science and engineering education.

Guides students through analyzing an article, emphasizing paraphrasing and note-taking.

Explores the rise of share-holder activism, its impact on market performance, and the challenges faced by arbitrageurs in correcting mispricings.

Introduces a mini-mook on Implementation Research to enhance competencies in addressing infectious diseases of poverty.

Discusses a take-home exam focusing on identifying and analyzing relevant concepts in case studies.

Covers the preparation for a take-home exam, focusing on analyzing case studies and identifying relevant concepts for assessment.

Delves into the concept of proof in mathematics, emphasizing the importance of evidence and logical reasoning.

Delves into the integrative ideal of 4Ps Medicine, focusing on big data, personalized health, and evidence-based challenges.

Teaches students how to read research articles actively and efficiently in social sciences, emphasizing engaging with the argument and evidence.

Delves into the benefits of active processing of information and the crucial role of feedback in learning.

Explores the integration of computational thinking in education and the effectiveness of a flipped classroom approach.

Emphasizes the importance of patient-centered medical device design and interdisciplinary collaboration in health monitoring.

Delves into ethics and law in AI, focusing on values, normative ambitions, and ethical challenges.

Covers the Bayes discriminant rule for allocating individuals to populations based on measurements and prior probabilities.