Choosing what works for whom: towards a better use of mechanistic knowledge in clinical practice

Background Clinicians commonly try to use mechanism-based knowledge to make sense of the complexity and uncertainty of chronic pain treatments to create a rationale for their clinical decision-making. Although this seems intuitive, there are some problems with this approach. Discussion The widespread use of mechanism-based knowledge in clinical practice can be a source of confusion for clinicians, especially when complex interventions with different proposed mechanisms of action are equally effective. Although the available mechanistic evidence is still of very poor quality, in choosing from various treatment options for people with chronic pain, an approach that correctly incorporates mechanistic reasoning might aid clinical thinking and practice. Conclusion By explaining that not all evidence of mechanism is the same and by making a proposal to start using mechanism-based knowledge in clinical practice properly, we hope to help clinicians to incorporate mechanistic reasoning to prioritize and start choosing what may best work for whom.

Stimulating Mechanistic Reasoning in Physics Using Student-Constructed Stop-Motion Animations

This article reports on a case study that aims to help students develop mechanistic reasoning through constructing a model based stop-motion animation of a physical phenomenon. Mechanistic reasoning is a valuable thinking strategy for students in trying to make sense of scientific phenomena. Ten ninth-grade students used stop-motion software to create an animation of projectile motion. Retrospective think-aloud interviews were conducted to investigate how the construction of a stop-motion animation induced the students’ mechanistic reasoning. Mechanistic reasoning did occur while the students engaged in creating the animation, in particular chunking and sequencing. Moreover, all students eventually exhibited mechanistic reasoning including abstract concepts, e.g., not directly observable agents. Students who reached the highest level of mechanistic reasoning, i.e., chaining, demonstrated deeper conceptual understanding of content.

Mechanistic reasoning and the problem of masking

At least historically, it was common for medical practitioners to believe causal hypotheses on the basis of standalone mechanistic reasoning. However, it is now widely acknowledged that standalone mechanistic reasoning is insufficient for appropriately believing a causal hypothesis in medicine, thanks in part to the so-called problem of masking. But standalone mechanistic reasoning is not the only type of mechanistic reasoning. When exactly then is it appropriate to believe a causal hypothesis on the basis of mechanistic reasoning? In this paper, I argue that it has proved difficult to provide a satisfying answer to this question. I also argue that this difficulty is predicted by recent work in knowledge-first epistemology. I think this shows that recent work in epistemology has important implications for practice in the philosophy of science. It is therefore worth paying closer attention in the philosophy of science to this recent work in knowledge-first epistemology.

Let's frame it differently – analysis of instructors’ mechanistic explanations

Research in Organic Chemistry education has revealed students’ challenges in mechanistic reasoning. When solving mechanistic tasks, students tend to focus on explicit surface features, apply fragmented conceptual knowledge, rely on rote-memorization and, hence, often struggle to build well-grounded causal explanations. When taking a resource perspective as a lens, students’ difficulties may arise from either an unproductive or a missing activation of cognitive resources. Instructors’ explanations and their guidance in teaching situations could serve as a lynchpin to activate these resources. Compared to students’ challenges in building mechanistic explanations in Organic Chemistry, little is known about instructors’ explanations when solving mechanistic tasks and how they shape their targeted explanations for students in terms of the construction and embedding of cause–effect rationales. This qualitative study aims to contribute to the growing research on mechanistic reasoning by exploring instructors’ explanatory approaches. Therefore, we made use of the framing construct, intended to trigger certain frames with explicit instruction. Ten Organic Chemistry instructors (university professors and lecturers) were asked to solve case comparison tasks while being prompted in two scenarios: an expert frame and a teaching frame. Our analysis shows that there is a shift from instructors’ mechanistic explanations in the expert frame towards more elaborated explanations in the teaching frame. In the teaching frame, contrary to what might be expected, complete cause–effect relationships were not always established and instructors differed in how they shaped their explanations. Additional explanatory elements were identified in both frames and their shift in use is discussed. Comparing approaches between frames sheds light on how instructors communicate mechanistic explanations and allows us to derive implications for teaching Organic Chemistry.