Learning the facts in medical school is not enough: which factors predict successful application of procedural knowledge in a laboratory setting?

BMC Medical Education

Martin R Fischer¹  Matthias Holzer¹  Inga Hege¹  Miriam Schiller²  Rene Ebersbach²  Stephan Eiber²  Ralf Schmidmaier² 

[1] Lehrstuhl für Didaktik und Ausbildungsforschung (DAM) in der Medizin am Klinikum der Ludwig-Maximilians-Universität München, Munich, Germany;Klinikum der Universität München (LMU), Medizinische Klinik und Poliklinik IV, Ziemssenstr. 1, 80336, Munich, Germany

关键词: Prior cognitive performance;    Clinical experience;    Problem solving task;    Key feature problems;    Conditional knowledge;    Strategic knowledge;    Procedural knowledge;    Conceptual knowledge;   

Others  :  1138996
DOI  :  10.1186/1472-6920-13-28

 received in 2012-04-06, accepted in 2013-02-20,  发布年份 2013

【 摘 要 】

Background

Medical knowledge encompasses both conceptual (facts or “what” information) and procedural knowledge (“how” and “why” information). Conceptual knowledge is known to be an essential prerequisite for clinical problem solving. Primarily, medical students learn from textbooks and often struggle with the process of applying their conceptual knowledge to clinical problems. Recent studies address the question of how to foster the acquisition of procedural knowledge and its application in medical education. However, little is known about the factors which predict performance in procedural knowledge tasks. Which additional factors of the learner predict performance in procedural knowledge?

Methods

Domain specific conceptual knowledge (facts) in clinical nephrology was provided to 80 medical students (3^rd to 5^th year) using electronic flashcards in a laboratory setting. Learner characteristics were obtained by questionnaires. Procedural knowledge in clinical nephrology was assessed by key feature problems (KFP) and problem solving tasks (PST) reflecting strategic and conditional knowledge, respectively.

Results

Results in procedural knowledge tests (KFP and PST) correlated significantly with each other. In univariate analysis, performance in procedural knowledge (sum of KFP+PST) was significantly correlated with the results in (1) the conceptual knowledge test (CKT), (2) the intended future career as hospital based doctor, (3) the duration of clinical clerkships, and (4) the results in the written German National Medical Examination Part I on preclinical subjects (NME-I). After multiple regression analysis only clinical clerkship experience and NME-I performance remained independent influencing factors.

Conclusions

Performance in procedural knowledge tests seems independent from the degree of domain specific conceptual knowledge above a certain level. Procedural knowledge may be fostered by clinical experience. More attention should be paid to the interplay of individual clinical clerkship experiences and structured teaching of procedural knowledge and its assessment in medical education curricula.

【 授权许可】

   
2013 Schmidmaier et al; licensee BioMed Central Ltd.

【 图 表 】

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【 参考文献 】

• [1]Considine J, Botti M, Thomas S: Do knowledge and experience have specific roles in triage decision-making? Acad Emerg Med 2007, 14:722-726.
• [2]Wimmers PF, Splinter TA, Hancock GR, Schmidt HG: Clinical competence: general ability or case-specific? Adv Health Sci Educ Theory Pract 2007, 12:299-314.
• [3]Wimmers PF, Fung CC: The impact of case specificity and generalisable skills on clinical performance: a correlated traits-correlated methods approach. Med Educ 2008, 42:580-588.
• [4]Krathwohl DR: A revision of bloom's taxonomy: an overview. Theory into Practice 2002, 41:212-218.
• [5]van Gog T, Paas F, van Merrienboer JJG: Process-oriented worked examples: improving transfer performance through enhanced understanding. Instr Sci 2004, 32:83-98.
• [6]Ohlsson S, Rees E: The function of conceptual understanding in the learning of arithmetic procedures. Cogn Instr 1991, 8:103-179.
• [7]Kopp V, Stark R, Kuhne-Eversmann L, Fischer MR: Do worked examples foster medical students' diagnostic knowledge of hyperthyroidism? Med Educ 2009, 43:1210-1217.
• [8]Joseph GM, Patel VL: Domain knowledge and hypothesis generation in diagnostic reasoning. Med Decis Making 1990, 10:31-46.
• [9]Patel VL, Arocha JF, Kaufman DR: Diagnostic reasoning and medical expertise. The psychology of learning and motivation San Diego, CA: Academic Press 1994, 31:187-252.
• [10]Elstein AS, Shulman LS, Sprafka SA: Medical Problem Solving: an Analysis of Clinical Reasoning. Cambridge, MA: Harvard University Press; 1978.
• [11]Neufeld VR, Norman GR: Assessing Clinical Competence. New York: Springer; 1985.
• [12]van der Vleuten CP, Swanson DB: Assessment of clinical skills with standardised patients: state of the art. Teach Learn Med 1990, 2:58-76.
• [13]Kopp V, Stark R, Fischer MR: Fostering diagnostic knowledge through computer-supported, case-based worked examples: effects of erroneous examples and feedback. Med Educ 2008, 42:823-829.
• [14]Allen EB, Walls RT, Reilly FD: Effects of interactive instructional techniques in a web-based peripheral nervous system component for human anatomy. Med Teach 2008, 30:40-47.
• [15]Kornell N, Son LK: Learners' choices and beliefs about self-testing. Memory 2009, 17:493-501.
• [16]Schmidmaier R, Ebersbach R, Schiller M, Hege I, Holzer M, Fischer MR: Using electronic flashcards to promote learning in medical students: retesting versus restudying. Med Educ 2011, 45:1101-1110.
• [17]Bordage G, Brailovsky C, Carretier H, Page G: Content validation of key features on a national examination of clinical decision-making skills. Acad Med 1995, 70:276-281.
• [18]Hatala R, Norman GR: Adapting the key features examination for a clinical clerkship. Med Educ 2002, 36:160-165.
• [19]Farmer EA, Hinchy J: Assessing general practice clinical decision making skills: the key features approach. Aust Fam Physician 2005, 34:1059-1061.
• [20]Farmer EA, Page G: A practical guide to assessing clinical decision-making skills using the key features approach. Med Educ 2005, 39:1188-1194.
• [21]Meterissian SH: A novel method of assessing clinical reasoning in surgical residents. Surg Innov 2006, 13:115-119.
• [22]Fischer MR, Kopp V, Holzer M, Ruderich F, Junger J: A modified electronic key feature examination for undergraduate medical students: validation threats and opportunities. Med Teach 2005, 27:450-455.
• [23]Page G, Bordage G, Allen T: Developing key-feature problems and examinations to assess clinical decision-making skills. Acad Med 1995, 70:194-201.
• [24]Graber ML: Educational strategies to reduce diagnostic error: can you teach this stuff? Adv Health Sci Educ Theory Pract 2009, 14(Suppl 1):63-69.
• [25]Gräsel C, Mandl H: Förderung des Erwerbs diagnostischer Strategien in fallbasierten Lernumgebungen [Promoting the acquisition of diagnostic strategies in case-based learning environments]. Unterrichtswissenschaft 1993, 21:355-369.
• [26]Norman G, Young M, Brooks L: Non-analytical models of clinical reasoning: the role of experience. Med Educ 2007, 41:1140-1145.
• [27]Charlin B, Tardif J, Boshuizen HP: Scripts and medical diagnostic knowledge: theory and applications for clinical reasoning instruction and research. Acad Med 2000, 75:182-190.
• [28]Berner ES, Graber ML: Overconfidence as a cause of diagnostic error in medicine. Am J Med 2008, 121:S2-S23.
• [29]Benbassat J, Baumal R: Uncertainties in the selection of applicants for medical school. Adv Health Sci Educ Theory Pract 2007, 12:509-521.
• [30]Ferguson E, James D, Madeley L: Factors associated with success in medical school: systematic review of the literature. BMJ 2002, 324:952-957.
• [31]Hampe W, Hissbach J, Kadmon M, Kadmon G, Klusmann D, Scheutzel P: [Who will be a good physician? Admission procedures for medical and dental students]. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz 2009, 52:821-830.
• [32]Hissbach JC, Klusmann D, Hampe W: Dimensionality and predictive validity of the HAM-Nat, a test of natural sciences for medical school admission. BMC Med Educ 2011, 11:83. BioMed Central Full Text
• [33]Gentsch S: Richtig ausgewaehlt?: eine Evaluation neuer Verfahren der Studierendenauswahl in den Faechern Medizin und Pharmazie an der Goethe-Universitaet. Berlin: Logos-Verl; 2009.
• [34]Fischer MR, Schauer S, Grasel C, Baehring T, Mandl H, Gartner R, Scherbaum W, Scriba PC: [CASUS model trial. A computer-assisted author system for problem-oriented learning in medicine]. Z Arztl Fortbild 1996, 90:385-389.
• [35]Simonsohn AB, Fischer MR: [Evaluation of a case-based computerized learning program (CASUS) for medical students during their clinical years]. Dtsch Med Wochenschr 2004, 129:552-556.
• [36]Kolb DA, Smith DM: Learning style inventory: a manual for teachers and trainers. User's guide. Boston, Mass: McBer 1986, 105-119.
• [37]Stark RKV, Fischer MR: Cased-based learning with worked examples in complex domains: two experimental studies in undergraduate medical education. Learn Instr 2011, 21:22-33.
• [38]Ross BH: This is like that: the use of earlier problems and the separation of similarity effects. J Exp Psychol Learn Mem Cogn 1987, 13(4):629-639.
• [39]Kulasegaram K, Min C, Ames K, Howey E, Neville A, Norman G: The effect of conceptual and contextual familiarity on transfer performance. Adv Health Sci Educ Theory Pract 2011.
• [40]Boshuizen HP, van der Vleuten CP, Schmidt HG, Machiels-Bongaerts M: Measuring knowledge and clinical reasoning skills in a problem-based curriculum. Med Educ 1997, 31:115-121.
• [41]Sobral DT: What kind of motivation drives medical students' learning quests? Med Educ 2004, 38:950-957.
• [42]Wilkinson TJ, Wells JE, Bushnell JA: Medical student characteristics associated with time in study: is spending more time always a good thing? Med Teach 2007, 29:106-110.
• [43]Moulaert V, Verwijnen MG, Rikers R, Scherpbier AJ: The effects of deliberate practice in undergraduate medical education. Med Educ 2004, 38:1044-1052.
• [44]Price J, Williams G, Wiltshire EB: Influence of motivational and demographic factors on performance in the medical course: a prospective study. Med Educ 1994, 28:107-115.
• [45]Cohen-Schotanus J, Muijtjens AM, Reinders JJ, Agsteribbe J, van Rossum HJ, van der Vleuten CP: The predictive validity of grade point average scores in a partial lottery medical school admission system. Med Educ 2006, 40:1012-1019.
• [46]Woodward CA, McAuley RG: Characteristics of medical students who choose primary care as a career: the McMaster experience. Can Med Assoc J 1984, 130:129-131.
• [47]Burkett GL, Gelula MH: Characteristics of students preferring family practice/primary care careers. J Fam Pract 1982, 15:505-512.
• [48]Wilkinson TJ, Wells JE, Bushnell JA: Using a diary to quantify learning activities. Med Educ 2005, 39:657-664.
• [49]Cook DA, Triola MM: Virtual patients: a critical literature review and proposed next steps. Med Educ 2009, 43:303-311.
• [50]Diemers AD, Dolmans DH, Verwijnen MG, Heineman E, Scherpbier AJ: Students' opinions about the effects of preclinical patient contacts on their learning. Adv Health Sci Educ Theory Pract 2008, 13:633-647.