Identifying Students Causal Mechanistic Reasoning (CMR) in Medical Pharmacology [ASPET 2023 Annual Meeting Abstract - Pharmacology Education]

Abstract ID 18675

Poster Board 593

Background: Many medical students struggle with the overload of information in pharmacology and lack knowledge of foundational pharmacology principles compared to pharmacy students. Education research states that when students use epistemic heuristics such as causal mechanistic reasoning, they can better construct precise inferences and are more likely to make strong predictions on new phenomena than those who learn using recall and memorization methods.

Hypothesis: students using CMR to understand pharmacologic pathways can develop predictive insights regarding currently taught pharmacologic agents and novel ones they will approach in the clinic

The study AIMED to: investigate how students respond to a prompt designed to elicit causal mechanistic reasoning about the adverse effect of an adrenergic antagonist and Na+-glucose-like type-2 transporter inhibitor; analyze the extent to which students use CMR to explain pharmacological phenomena.

Methods: short-answer responses were collected from 100-200 2nd-year osteopathic medical students enrolled at a large midwestern university (IRB approval: 7350). Demographic information was collected; personal information was de-identified. Students were asked about: pharmacology education/background, undergrad major, research or job experience, and clinical experience and to rank their study materials and methods. We are analyzing connections between this information and students' use of CMR, with particular attention to students' pharmacology background and study methods. Causal mechanistic approach: a coding scheme was developed to explore the sophistication responses in a problem scaffolded to elicit medical pharmacology knowledge. Based on data from a smaller subset of responses, two independent researchers coded the answers to establish inter-rater reliability (IRR), calculated via Cohen's kappa values to collect qualitative and quantitative data. The qualitative data of the binned responses were analyzed for independence via the Pearson Chi-square test and their level of CMR via the Chi-square test.

Results: 25% of the students could provide a fully causal mechanistic account of the phenomenon. Still, 50% of the students could provide at least some part of the CMR model in their explanation. No significant relationship (p=0.17) was found when comparing the pharmacology background (None, Moderate, or Strong) with whether they correctly predicted the phenomenon. Similarly, no significant association (p=0.2) was found when comparing their primary study resource (Active, Passive, or Memorization) with whether they correctly predicted the phenomenon.

Conclusion: Students primarily use recall for pharmacologic information, particularly with identifying drugs' adverse effects, rather than using a CMR model. Students using recall methods are less likely to integrate their knowledge regarding pharmacological phenomena and make robust predictions about novel treatments. While there is no significant association between the correctness of the students’ prediction and their pharmacology background or methods of studying, there is a significant relationship between their prediction correctness and their engagement in CMR.

Perspectives: Collectively, our data’s evidence supports that using a more integrative, pathway-based pharmacology curriculum would allow students to use CMR when engaging with agents/drugs pharmacological pathways.

Support Info: MSUCOM Dep Pharm & Tox

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