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Publication details
A taxonomy and framework for identifying and developing actionable statements in guidelines suggests avoiding informal recommendations
Authors | |
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Year of publication | 2022 |
Type | Article in Periodical |
Magazine / Source | Journal of clinical epidemiology |
MU Faculty or unit | |
Citation | |
web | https://www.jclinepi.com/article/S0895-4356(21)00314-0/fulltext#relatedArticles |
Doi | http://dx.doi.org/10.1016/j.jclinepi.2021.09.028 |
Keywords | Guidelines; Standards; Recommendations; GRADE; Practice statements; Policy |
Description | Objective To propose a taxonomy and framework that identifies and presents actionable statements in guidelines. Study design and setting We took an iterative approach reviewing case studies of guidelines produced by the World Health Organization and the American Society of Hematology to develop an initial conceptual framework. We then tested it using randomly selected recommendations from published guidelines addressing COVID-19 from different organizations, evaluated its results, and refined it before retesting. The urgency and availability of evidence for development of these recommendations varied. We consulted with experts in research methodology and guideline developers to improve the final framework. Results The resulting taxonomy and framework distinguishes five types of actional statements: formal recommendations; research recommendations; good practice statements; implementation considerations, tools and tips; and informal recommendations. These statements should respond to a priori established criteria and require a clear structure and recognizable presentation in a guideline. Most importantly, this framework identifies informal recommendations that differ from formal recommendations by how they consider evidence and in their development process. Conclusion The identification, standardization and explicit labelling of actionable statements according to the framework may support guideline developers to create actionable statements with clear intent, avoid informal recommendations and improve their understanding and implementation by users. |