Bmc Med Res Methodol
-
Bmc Med Res Methodol · Jun 2020
Applying an intersectionality lens to the theoretical domains framework: a tool for thinking about how intersecting social identities and structures of power influence behaviour.
A key component of the implementation process is identifying potential barriers and facilitators that need to be addressed. The Theoretical Domains Framework (TDF) is one of the most commonly used frameworks for this purpose. When applying the TDF, it is critical to understand the context in which behaviours occur. Intersectionality, which accounts for the interface between social identity factors (e.g. age, gender) and structures of power (e.g. ageism, sexism), offers a novel approach to understanding how context shapes individual decision-making and behaviour. We aimed to develop a tool to be used alongside applications of the TDF to incorporate an intersectionality lens when identifying implementation barriers and enablers. ⋯ Through an expert-consensus approach, we developed a tool for applying an intersectionality lens alongside the TDF. Considering the role of intersecting social factors when identifying barriers and facilitators to implementing research evidence may result in more targeted and effective interventions that better reflect the realities of those involved.
-
Bmc Med Res Methodol · Jun 2020
Research methodology and characteristics of journal articles with original data, preprint articles and registered clinical trial protocols about COVID-19.
The research community reacted rapidly to the emergence of COVID-19. We aimed to assess characteristics of journal articles, preprint articles, and registered trial protocols about COVID-19 and its causal agent SARS-CoV-2. ⋯ Early articles on COVID-19 were predominantly retrospective case reports and modeling studies. The diversity of outcomes used in intervention trial protocols indicates the urgent need for defining a core outcome set for COVID-19 research. Chinese scholars had a head start in reporting about the new disease, but publishing articles in Chinese may limit their global reach. Mapping publications with original data can help finding gaps that will help us respond better to the new public health emergency.
-
Bmc Med Res Methodol · Jun 2020
Towards reduction in bias in epidemic curves due to outcome misclassification through Bayesian analysis of time-series of laboratory test results: case study of COVID-19 in Alberta, Canada and Philadelphia, USA.
Despite widespread use, the accuracy of the diagnostic test for SARS-CoV-2 infection is poorly understood. The aim of our work was to better quantify misclassification errors in identification of true cases of COVID-19 and to study the impact of these errors in epidemic curves using publicly available surveillance data from Alberta, Canada and Philadelphia, USA. ⋯ The best way to better understand bias in the epidemic curves of COVID-19 due to errors in testing is to empirically evaluate misclassification of diagnosis in clinical settings and apply this knowledge to adjustment of epidemic curves.