News for thought – more reasons to stress fundamentals of team communication, two challenge rule, etc…
News for thought – more reasons to stress fundamentals of team communication, two challenge rule, etc…
3 up for CHSE and 3 certified — I’d like to think of myself as a nice Brie cheese perhaps….
Anyhow – we started talking about this last week:
From McGaghie et al – Translational Medicine, February 2010, Vol2, issue 9
——————————– T1 T2 T3
increased/improved KSA, profesionalism Patient Care Patient outcomes
Target Individ. And teams Individ. And teams Individ. & public health
Setting: simulation Lab clinic and bedside clinic and community
|Gaba||So you taught them something, can they do it when you give them the simulation?||You taught them something in simulation, when they go out on the wards, do they really do it – the way you taught them?Ability to measure this..||Does it really change patient outcome?Does it save money?
Just because you show something works in one study, does it mean it can be disseminated and it can be done by others?
Does it really change the outcome of populations as a whole to do these things?
Simulation is a form of case based learning.
Case based learning grew out of the problem based learning discipline. PBL employs an open inquiry approach, in which students independently discover knowledge within a domain. Self directed learning is central to a PBL curriculum. The knowledge base is integrated through the discovery of its applicability across cases and problems. Barrows argues that the core of problem based learning lies in allowing students to “analyze and resolve the problem as far as possible before acquiring any information needed for better understanding.”(1)
PBL’s proponents have argued that it encourages lifelong learning, fosters the development of superior problem solving skills, and is firmly grounded in adult learning theory (self-directed, building on prior experience, relevant to the lives and work of learners). Critics of problem-based learning have argued that the open inquiry approach is inefficient, wastes faculty time, and leads to sometimes inaccurate or erroneous constructs which the learner establishes out of inexperience or lack of knowledge. Despite its theoretical strengths, learning outcomes in problem based learning curricula are mixed, leading educators to speculate on and attempt to mitigate the shortcomings of PBL and the reasons for the disconnect between theory and outcome.(2)
Case based learning leverages the theoretical underpinnings of PBL, but adopts a guided inquiry approach, in which the expertise of the instructor is used to guide the discussion toward relevant and accurate knowledge, and to mitigate group dysfunction that inhibits learning. Case based teaching requires both content expertise and expertise in group facilitation.
Case based learning has its critics as well, and they argue that the “guided” nature of CBL stifles creativity, and that the guidance may well be ineffective unless there is adequate attention to faculty development.
In a comparison of satisfaction and perceived value of PBL vs. CBL at two medical schools, Srinivasan et al reported that students and faculty overwhelmingly preferred CBL. Students felt that CBL was more efficient, provided better opportunity for applying skills learned, and provided valuable feedback.(3)
Simulation is grounded in the philosophies of case based teaching and learning, using the experience of the simulation to provide active engagement with the case, and guided inquiry to achieve the reflective observation necessary for learning.
In her primer on cases based teaching, Colich(4) reviews attributes and advantages of case based teaching. At its best, simulation will share these attributes. Learners will make and implement decisions by sorting out pertinent information from irrelevant information, they will apply prior knowledge to identify core problems, and they will formulate narratives about problems and strategies to address them. The learning outcomes (knowledge, skills and attitudes) match the ability of the case to challenge students in these areas.
Case based learning is a constructivist approach. Constructivism relies on the notions that learning is based on interactions with the environment, that cognitive puzzlement is a powerful stimulus for learning, and that social negotiation is an important contributor to knowledge acquisition.(5) Simulation is grounded in these ideas and in the principles of designing an authentic task, anchoring the learning in a larger problem, and providing opportunity for guided reflection. The idea of social negotiation is particularly interesting in its application to the desired learning for the simulation. Teaching the need to “speak up”, to avoid assumptions and to engage in error correction are substantial challenges for medical educators. The collaborative learning process gives us an opportunity to reinforce the notion that lack of comment or question implies agreement.
1. Barrow HS. Problem based Learning in Medicine and Beyond: A Brief Overview. New Directions for Teaching and learning 1996; 68: 3-12
2. Onyon C. Problem-based learning a review of the educational and psychological theory. The Clinical Teacher 2012; 9: 22-26
3. Srinivasan M. Comparing problem-based learning with case-based learning. Acad Med 2007; 82(1): 74-82
4. Golich C. The ABC’s of Case Based Teaching. International studies perspectives 2000; 1: 11-29.
5. Savery J and Duffy T. Problem based learning: An instructional model and its constructivist framework. in Wilson, BG. Constructivist learning environments: case studies in instructional design. Educational Technology Publications Inc., Englewood Cliffs, NJ. 1996
Interesting articles, trying to figure out how all of these ideas apply to medical simulation
(truth be told we didn’t read all of them – just some…)
1 . Manipulation of cognitive load variables and impact on auscultation test performance Ruth Chen • Lawrence Grierson • Geoffrey Norman Received: 20 January 2014 / Accepted: 20 November 2014
2. 4C/ID in medical education: How to design an educational program based on whole-task learning: AMEE Guide No. 93
3. Mental load: helping clinical learners Geoff White, Clinical Education and Professional Development Unit, School of Primary Health Care, Monash University, Australia
4. Cognitive load theory in health professional education: design principles and strategies. Medical Education 2010: 44: 85–93
5. Cognitive Load Theory: Implications for medical education: AMEE Guide No. 86 (more overview wtih examples)
Extraneous Load – load not essential to the task
Intrinsic Load – load associated with the task
Germane Load – available working memory to learn and to deal with the extraneous and intrinsic load — these are elements that allow cognitive resources to be put towards learning/problem solving i.e. assist with information processing.
We should think about each of these as we tailor our learning experiences at different levels of learners. In health care – as opposed to powerpoint slides – there is always going to be a lot of extraneous load – and we do need to teach learners how to deal with this part of the tax on our working memories.
Nice pictures from article # 4 that show how overloading the intrinsic or extrinsic load can leave not potential germane load left for learners to work with in particular situation
A nice alternate way to think about cog load in reference to slides in powerpoint (but I can abstract this to similar “noise” in a simulation scenario)
Have to vary your learning instructional strategies for novices vs. experts…
Article #2 provides a detailed look at how to provide whole task learning – using cognitive load theory at it’s backbone. This is not just simulation but multiple ways of learning – that take a scaffoldign approach of single or simple task to more complex or multiple task – while at the same time (if I am interpreting this right) allowing for more direct coaching at the beginning and then more hands off coaching at the end – as learners go from novice to expert or at least more advanced. Contained within this schema is continual assessment, feedback and reflection.
Interesting approach – starting with a worked example – “show them what you want them to do” then break this down into sizabel chunks – that progressively get more difficult as the learning continues.
I had to read this article several times. I will probable need to read if a few more times.
Article #4 talks about strategies to minimize extraneous load and minimize intrinsic load for novice learners, and how to increase intrinsic load (and thus germane load) as learner expertise increases.
Minimizing extraneous load for novices:
– starting with goal free strategies (generate a list of as many diagnostic possibilities as you can) and moving to goal directed (what is the most likely diagnosis).
-starting with worked examples (i.e. demonstrations of how to do the task) then moving to completing more and more of a task – I liken this to learning a new computer software by trial and error – clicking button sequences until you finally get it right. It’s inefficient and frustrating. Much better to show the completed task and then let the learner complete a similar one
-Reinforce material by using multimodal presentation
A deconstructed roadmap for moving from unconscious incompetence to unconscious competence
– for next week – articles coming soon
Boulet JR, Jeffries PR, Hatala RA, Korndorffer JR, . Feinstein DM, Roche JP. Research Regarding Methods of Assessing Learning Outcomes Sim Healthcare 6:548-551,2011
Simulation based assessments are used for both formative and summative assessments of healthcare providers. The objective of this article was to look at how their use is supported and to provide direction in the form of consensus recommendations for research. . They delve into “the four components of Kane’s inferential chain –Scoring, Generalization, Extrapolation, and Decision/Interpretation.” The importance of the need for reliability as well as validity of simulation based assessments is emphasized within a brief review of existing research (as of 2011) and opportunities for additional work. Their recommendations target five areas for research which include measurement error, developing scoring rubrics, supporting the underlying theories for learning with simulation, translation of simulation based learning to the clinical environment, and the impact of implementing these assessments for healthcare.