Quality specifications in postgraduate medical e-learning: an integrative literature review leading to a postgraduate medical e-learning model

BACKGROUND: E-learning is driving major shifts in medical education. Prioritizing learning theories and quality models improves the success of e-learning programs. Although many e-learning quality standards are available, few are focused on postgraduate medical education. METHODS: We conducted an integrative review of the current postgraduate medical e-learning literature to identify quality specifications. The literature was thematically organized into a working model. RESULTS: Unique quality specifications (n = 72) were consolidated and re-organized into a six-domain model that we called the Postgraduate Medical E-learning Model (Postgraduate ME Model). This model was partially based on the ISO-19796 standard, and drew on cognitive load multimedia principles. The domains of the model are preparation, software design and system specifications, communication, content, assessment, and maintenance. CONCLUSION: This review clarified the current state of postgraduate medical e-learning standards and specifications. It also synthesized these specifications into a single working model. To validate our findings, the next-steps include testing the Postgraduate ME Model in controlled e-learning settings

Directed acyclic graphs: A tool to identify confounders in orthodontic research, Part I

Confounding is a bias that threatens the validity of causal inferences in a study. Rothman and Greenland1 defined confounding: “A confounding factor must not be affected by the exposure or disease. In particular, it cannot be an intermediate step in the causal pathway between the exposure and the disease.

Directed acyclic graphs: A tool to identify confounders in orthodontic research, Part II

In the previous article, we discussed the problem of confounding and presented 3 fundamental methods for assessing and adjusting for confounders: the traditional approach, the noncollapsibility approach, and the directed acyclic graphs (DAGs) or causal diagrams approach