Evidence for validity for the Cognitive Load Inventory for Handoffs. Issue 2 (7th September 2020)

• Record Type: Journal Article
• Authors: Young, John Q.; John, Majnu; Thakker, Krima; Friedman, Karen; Sugarman, Rebekah; Sewell, Justin L.; O'Sullivan, Patricia S.
• Is Part Of: Medical education. Volume 55:Issue 2(2021)
• Page Start: 222
• Page End: 232
• Publication Date: 2020-09-07
• Subjects: Medical education -- Periodicals; Medical education -- Great Britain -- Periodicals; 610.7
• DOI: 10.1111/medu.14292
• Languages: English
• ISSNs: 0308-0110

Abstract

Abstract: Context: Patient 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. Methods: The 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). Results: Participants 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. Conclusions: The 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. Abstract : 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.