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.

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Introduction Mobile apps that utilize the framework of entrustable professional activities (EPAs) to capture and deliver feedback are being implemented. If EPA apps are to be successfully incorporated into programmatic assessment, a better understanding of how they are experienced by the end-users will be necessary. The authors conducted a qualitative study using the Consolidated Framework for Implementation Research (CFIR) to identify enablers and barriers to engagement with an EPA app. Methods Structured interviews of faculty and residents were conducted with an interview guide based on the CFIR. Transcripts were independently coded by two study authors using directed content analysis. Differences were resolved via consensus. The study team then organized codes into themes relevant to the domains of the CFIR. Results Eight faculty and 10 residents chose to participate in the study. Both faculty and residents found the app easy to use and effective in facilitating feedback immediately after the observed patient encounter. Faculty appreciated how the EPA app forced brief, distilled feedback. Both faculty and residents expressed positive attitudes and perceived the app as aligned with the department's philosophy. Barriers