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Evidence for validity for the Cognitive Load Inventory for Handoffs

dc.contributor.authorYoung, John Q.
dc.contributor.authorJohn, Majnu
dc.contributor.authorThakker, Krima
dc.contributor.authorFriedman, Karen
dc.contributor.authorSugarman, Rebekah
dc.contributor.authorSewell, Justin L.
dc.contributor.authorO’sullivan, Patricia S.
dc.date.accessioned2021-02-04T21:54:09Z
dc.date.available2022-03-04 16:54:08en
dc.date.available2021-02-04T21:54:09Z
dc.date.issued2021-02
dc.identifier.citationYoung, John Q.; John, Majnu; Thakker, Krima; Friedman, Karen; Sugarman, Rebekah; Sewell, Justin L.; O’sullivan, Patricia S. (2021). "Evidence for validity for the Cognitive Load Inventory for Handoffs." Medical Education (2): 222-232.
dc.identifier.issn0308-0110
dc.identifier.issn1365-2923
dc.identifier.urihttps://hdl.handle.net/2027.42/166269
dc.description.abstractContextPatient handovers remain a significant patient safety challenge. Cognitive load theory (CLT) can be used to identify the cognitive mechanisms for handover errors. The ability to measure cognitive load types during handovers could drive the development of more effective curricula and protocols. No such measure currently exists.MethodsThe authors developed the Cognitive Load Inventory for Handoffs (CLIH) using a multi- step process, including expert interviews to enhance content validity and talk- alouds to optimise response process validity. The final version contained 28 items. From January to March 2019, we administered a cross- sectional survey to 1807 residents and fellows from a large health care system in the USA. Participants completed the CLIH following a handover. Exploratory factor analysis of data from one- third of respondents identified high- performing items; confirmatory factor analysis of data from the remaining sample assessed model fit. Model fit was evaluated using the comparative fit index (CFI) (>0.90), Tucker- Lewis index (TFI) (>0.80), standardised root mean square residual (SRMR) (<0.08) and root mean square of error of approximation (RMSEA) (<0.08).ResultsParticipants included 693 trainees (38.4%) (231 in the exploratory study and 462 in the confirmatory study). Eleven items were removed during exploratory factor analysis. Confirmatory factor analysis of the 16 remaining items (five for intrinsic load, seven for extraneous load and four for germane load) supported a three- factor model and met criteria for good model fit: the CFI was 0.95, TFI was 0.93, RMSEA was 0.074 and SRMR was 0.07. The factor structure was comparable for gender and role. Intrinsic, extraneous and germane load scales had high internal consistency. With one exception, scale scores were associated, as hypothesised, with postgraduate level and clinical setting.ConclusionsThe CLIH measures three types of cognitive load during patient handovers. Evidencefor validity is provided for the CLIH’s content, response process, internal structure and association with other variables. This instrument can be used to determine the relative drivers of cognitive load during handovers in order to optimize handover instruction and protocols.Cognitive load is generally recognized as needing to be managed during learning interactions, but is difficult to measure in practice. Here, Young et al. offer evidence regarding how to do so during patient handoffs.
dc.publisherWiley Periodicals, Inc.
dc.publisherOxford University Press
dc.titleEvidence for validity for the Cognitive Load Inventory for Handoffs
dc.typeArticle
dc.rights.robotsIndexNoFollow
dc.subject.hlbsecondlevelEducation
dc.subject.hlbsecondlevelMedicine (General)
dc.subject.hlbtoplevelHealth Sciences
dc.subject.hlbtoplevelSocial Sciences
dc.description.peerreviewedPeer Reviewed
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/166269/1/medu14292.pdf
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/166269/2/medu14292_am.pdf
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/166269/3/medu14292-sup-0003-supinfo.pdf
dc.identifier.doi10.1111/medu.14292
dc.identifier.doihttps://dx.doi.org/10.7302/192
dc.identifier.sourceMedical Education
dc.identifier.citedreferenceStovel RG, Ginsburg S, Stroud L, Cavalcanti RB, Devine LA. Incentives for recruiting trainee participants in medical education research. Med Teach. 2018; 40 ( 2 ): 181 - 187.
dc.identifier.citedreferenceSweller J, van Merriënboer JJG, Paas F. Cognitive architecture and instructional design: 20 years later. Educ Psychol Rev. 2019; 31 ( 2 ): 261 - 292.
dc.identifier.citedreferenceSewell JL, Boscardin CK, Young JQ, ten Cate O, O’Sullivan PS. Measuring cognitive load during procedural skills training with colonoscopy as an exemplar. Med Educ. 2016; 50 ( 6 ): 682 - 692.
dc.identifier.citedreferenceYoung JQ, Irby DM, Barilla- LaBarca ML, ten Cate O, O’Sullivan PS. Measuring cognitive load: mixed results from a handover simulation for medical students. Perspect Med Educ. 2016; 5 ( 1 ): 24 - 32.
dc.identifier.citedreferenceYoung JQ, Boscardin CK, van Dijk SM, et al. Performance of a cognitive load inventory during simulated handoffs: evidence for validity. SAGE Open Med. 2016; 4: 2050312116682254.
dc.identifier.citedreferenceKane MT. Current concerns in validity theory. J Educ Meas. 2001; 38 ( 4 ): 319 - 342.
dc.identifier.citedreferenceDowning SM. Validity: on meaningful interpretation of assessment data. Med Educ. 2003; 37 ( 9 ): 830 - 837.
dc.identifier.citedreferenceAmerican Educational Research Association, American Psychological Association, National Council on Measurement in Education. Standards for Educational and Psychological Testing. Washington, DC: AERA Publications; 2014.
dc.identifier.citedreferenceArtino AR Jr, La Rochelle JS, Dezee KJ, Gehlbach H. Developing questionnaires for educational research: AMEE Guide No. 87. Med Teach. 2014; 36 ( 6 ): 463 - 474.
dc.identifier.citedreferencevan Merrienboer JJG, Sweller J. Cognitive load theory in health professional education: design principles and strategies. Med Educ. 2010; 44 ( 1 ): 85 - 93.
dc.identifier.citedreferenceLeppink J, Paas F, van der Vleuten CP, van Gog T, van Merrienboer JJ. Development of an instrument for measuring different types of cognitive load. Behav Res Methods. 2013; 45 ( 4 ): 1058 - 1072.
dc.identifier.citedreferenceHarris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap) - a metadata- driven methodology and workflow process for providing translational research informatics support. J Biomed Informat. 2009; 42 ( 2 ): 377 - 381.
dc.identifier.citedreferenceDillman DA, Smyth JD, Christian LM. Internet, Phone, Mail, and Mixed- Mode Surveys: The Tailored Design Method. Hoboken, NJ: John Wiley & Sons; 2014.
dc.identifier.citedreferenceKyriazos T. Applied psychometrics: the 3- faced construct validation method, a routine for evaluating a factor structure. Psychology. 2018; 9(8): 2044 - 2072.
dc.identifier.citedreferenceWoods CM, Edwards MC. 6- Factor analysis and related methods. In: Rao CR, Miller JP, Rao DC, eds. Essential Statistical Methods for Medical Statistics. Boston, MA: North- Holland; 2011: 174 - 201.
dc.identifier.citedreferenceCook DA, Beckman TJ. Does scale length matter? A comparison of nine- versus five- point rating scales for the mini- CEX. Adv Health Sci Educ Theory Pract. 2009; 14 ( 5 ): 655 - 664.
dc.identifier.citedreferenceSullivan GM, Artino AR Jr. Analyzing and interpreting data from Likert- type scales. J Grad Med Educ. 2013; 5 ( 4 ): 541 - 542.
dc.identifier.citedreferenceHooper D, Coughlan J, Mullen M. Structural equation modelling: guidelines for determining model fit. Electr J Bus Res Methods. 2008; 6 ( 1 ): 53 - 60.
dc.identifier.citedreferenceHu L, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Struct Equat Model. 1999; 6 ( 1 ): 1 - 55.
dc.identifier.citedreferenceSewell JL, Maggio LA, ten Cate O, van Gog T, Young JQ, O’Sullivan PS. Cognitive load theory for training health professionals in the workplace: a BEME review of studies among diverse professions: BEME Guide No. 53. Med Teach. 2019; 41 ( 3 ): 256 - 270.
dc.identifier.citedreferenceLeppink J, van den Heuvel A. The evolution of cognitive load theory and its application to medical education. Perspect Med Educ. 2015; 4 ( 3 ): 119 - 127.
dc.identifier.citedreferencevan Merrienboer JJ, Sweller J. Cognitive load theory in health professional education: design principles and strategies. Med Educ. 2010; 44 ( 1 ): 85 - 93.
dc.identifier.citedreferenceKalyuga S. Cognitive load theory: how many types of load does it really need? Educ Psychol Rev. 2011; 23 ( 1 ): 1 - 19.
dc.identifier.citedreferenceFiorella L, Mayer RE. Eight ways to promote generative learning. Educ Psychol Rev. 2016; 28 ( 4 ): 717 - 741.
dc.identifier.citedreferenceRiesenberg LA, Leitzsch J, Massucci JL, et al. Residents’ and attending physicians’ handoffs: a systematic review of the literature. Acad Med. 2009; 84 ( 12 ): 1775 - 1787.
dc.identifier.citedreferenceVidyarthi AR, Arora V, Schnipper JL, Wall SD, Wachter RM. Managing discontinuity in academic medical centers: strategies for a safe and effective resident sign- out. J Hosp Med. 2006; 1 ( 4 ): 257 - 266.
dc.identifier.citedreferenceArora V, Johnson J, Lovinger D, Humphrey HJ, Meltzer DO. Communication failures in patient sign- out and suggestions for improvement: a critical incident analysis. Qual Saf Health Care. 2005; 14 ( 6 ): 401 - 407.
dc.identifier.citedreferenceArora VM, Johnson JK, Meltzer DO, Humphrey HJ. A theoretical framework and competency- based approach to improving handoffs. Qual Saf Health Care. 2008; 17 ( 1 ): 11 - 14.
dc.identifier.citedreferenceHorwitz LI, Moin T, Krumholz HM, Wang L, Bradley EH. Consequences of inadequate sign- out for patient care. Arch Intern Med. 2008; 168 ( 16 ): 1755 - 1760.
dc.identifier.citedreferenceGandhi TK, Kachalia A, Thomas EJ, et al. Missed and delayed diagnoses in the ambulatory setting: a study of closed malpractice claims. Ann Intern Med. 2006; 145 ( 7 ): 488 - 496.
dc.identifier.citedreferenceYoung JQ, Eisendrath SJ. Enhancing patient safety and resident education during the academic year- end transfer of outpatients: lessons from the suicide of a psychiatric patient. Acad Psychiatry. 2011; 35 ( 1 ): 54 - 57.
dc.identifier.citedreferenceStarmer AJ, O’Toole JK, Rosenbluth G, et al. Development, implementation, and dissemination of the I- PASS handoff curriculum: a multisite educational intervention to improve patient handoffs. Acad Med. 2014; 89 ( 6 ): 876 - 884.
dc.identifier.citedreferencePatterson ES, Roth EM, Woods DD, Chow R, Gomes JO. Handoff strategies in settings with high consequences for failure: lessons for health care operations. Int J Qual Health Care. 2004; 16 ( 2 ): 125 - 132.
dc.identifier.citedreferenceWohlauer MV, Arora VM, Horwitz LI, et al. The patient handoff: a comprehensive curricular blueprint for resident education to improve continuity of care. Acad Med. 2012; 87 ( 4 ): 411 - 418.
dc.identifier.citedreferenceStarmer AJ, Spector ND, Srivastava R, et al. Changes in medical errors after implementation of a handoff program. N Engl J Med. 2014; 371 ( 19 ): 1803 - 1812.
dc.identifier.citedreferenceYoung JQ, ten Cate O, O’Sullivan PS, Irby DM. Unpacking the complexity of patient handoffs through the lens of cognitive load theory. Teach Learn Med. 2016; 28 ( 1 ): 88 - 96.
dc.identifier.citedreferenceYoung JQ, Wachter RM, ten Cate O, O’Sullivan PS, Irby DM. Advancing the next generation of handover research and practice with cognitive load theory. BMJ Qual Saf. 2016; 25 ( 2 ): 66 - 70.
dc.identifier.citedreferenceSweller J. Cognitive load during problem solving: effects on learning. Cogn Sci. 1988; 12 ( 2 ): 257 - 285.
dc.identifier.citedreferenceSweller J, van Merrienboer JJG. Cognitive load theory and instructional design for medical education. In: Walsh K, ed. The Oxford Textbook of Medical Education. Oxford: Oxford University Press; 2013: 74 - 85.
dc.identifier.citedreferenceBaddeley A. Working memory: theories, models, and controversies. Annu Rev Psychol. 2012; 63(1): 1 - 29.
dc.identifier.citedreferenceCowan N. The magical number 4 in short- term memory: a reconsideration of mental storage capacity. Behav Brain Sci. 2001; 24 ( 1 ): 87 - 114; discussion 114- 185.
dc.identifier.citedreferenceYoung JQ, van Merrienboer J, Durning S, ten Cate O. Cognitive load theory: implications for medical education: AMEE Guide No. 86. Med Teach. 2014; 36 ( 5 ): 371 - 384.
dc.identifier.citedreferenceChoi H- H, van Merriënboer JJG, Paas F. Effects of the physical environment on cognitive load and learning: towards a new model of cognitive load. Educ Psychol Rev. 2014; 26 ( 2 ): 225 - 244.
dc.identifier.citedreferenceYoung JQ, Sewell JL. Applying cognitive load theory to medical education: construct and measurement challenges. Perspect Med Educ. 2015; 4 ( 3 ): 107 - 109.
dc.identifier.citedreferenceFeldon DF. Cognitive load and classroom teaching: the double- edged sword of automaticity. Educ Psychol. 2007; 42 ( 3 ): 123 - 137.
dc.identifier.citedreferenceSweller J, van Merrienboer JJG, Paas FGWC. Cognitive architecture and instructional design. Educ Psychol Rev. 1998; 10 ( 3 ): 251 - 296.
dc.identifier.citedreferenceLeppink J, Paas F, van Gog T, van der Vleuten CPM, van Merrienboer JJG. Effects of pairs of problems and examples on task performance and different types of cognitive load. Learn Instr. 2014; 30: 32 - 42.
dc.identifier.citedreferenceSweller J, Ayres PL, Kalyuga S. Cognitive Load Theory. New York, NY: Springer; 2011.
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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