Dunja Dreesens

92 welfare (221-223). These different types of reasoning try to help make valid inferences for the single-case scenario, when there is no frequency of events. Many of these are already recognised and stated by Bradford-Hill (224) in his criteria for causation, but some are newer, such as Annemarie Mol’s logic of care (225), where a practitioner will try something, wait and see and let unfolding events guide the next step. Using this type of reasoning, the problem of induction is solved through ‘tinkering’, making incremental changes to improve a situation. Guidelines can and do support these kinds of evasions by including different types of knowledge. For instance, providing laboratory information about aetiology helps to make an inference based on mechanistic reasoning (226). A description of cases of harm can offer an inference based on the precautionary principle (227). Rethinking how inferences are made in practice may shift the dominance of frequency-based reasoning and its reliance on a restrictive type of knowledge to a broader spectrum of knowledge being used to support different reasoning approaches. The need for using different type of knowledge is shown by a large Dutch analysis showing that knowledge from RCTs far outweighed other knowledge types used, irrespective of the question at hand, thus ignoring important and relevant knowledge from other sources (8, 216). The challenge of integration Making a recommendation for a specific healthcare problem in a specific healthcare system requires the assessment of knowledge not just on its own merits, but importantly its integration with other knowledge. Indeed, EBM is defined as integrating the best evidence with clinical expertise and patient preference (26). However, in the context of medicine, and even more so in that of guideline production, integration of different types of knowledge remains underexplored and undertheorized. Some areas of evidence synthesis have addressed integration. For example, statistical techniques such as meta-analysis can be used to combine data from different studies, and another range of techniques can be used to synthesise qualitative data. In guideline development, most of the activities and tools to support high-quality evidence synthesis such as risk of bias assessment and quality assessment (such as GRADE) tend to focus primarily on frequency-based reasoning and knowledge. For the assessment of quality of qualitative evidence, there are limited but relevant initiatives for guideline development in progress, for example, the recently published Grading of Recommendations Assessment, Development, and Evaluation – Confidence in the Evidence from Reviews of Qualitative research (GRADE-CERQual) (228) guidance. However, many of these efforts try to achieve integration by synthesizing studies that share the same questions and design (e.g. a set of qualitative or, more narrowly, ethnographic studies) (229), at times appraising (228) all such knowledge again in frequentist terms, like with some qualitative evidence synthesis methods (230) that “emphasize frequencies of the qualitative data they present … undermin[ing] the uniqueness of the qualitative knowledge they proclaim by focusing on frequency and the general patterns” (231). The main issue is that these tools, activities and initiatives aim to integrate similar knowledge, such as data from the same study designs, the same populations or the same outcomes. How different kinds of knowledge are valued, appraised and weighed in relation Chapter 5