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Warnings in early narrative assessment that might predict performance in residency: signal from an internal medicine residency program Cover

Warnings in early narrative assessment that might predict performance in residency: signal from an internal medicine residency program

Open Access
|Sep 2021

Abstract

Introduction Narrative assessment data are valuable in understanding struggles in resident performance. However, it remains unknown which themes in narrative data that occur early in training may indicate a higher likelihood of struggles later in training, allowing programs to intervene sooner.

Methods Using learning analytics, we identified 26 internal medicine residents in three cohorts that were below expected entrustment during training. We compiled all narrative data in the first 6 months of training for these residents as well as 13 typically performing residents for comparison. Narrative data were blinded for all 39 residents during initial phases of an inductive thematic analysis for initial coding.

Results Many similarities were identified between the two cohorts. Codes that differed between typical and lower entrusted residents were grouped into two types of themes: three explicit/manifest and three implicit/latent with six total themes. The explicit/manifest themes focused on specific aspects of resident performance with assessors describing 1) Gaps in attention to detail, 2) Communication deficits with patients, and 3) Difficulty recognizing the “big picture” in patient care. Three implicit/latent themes, focused on how narrative data were written, were also identified: 1) Feedback described as a deficiency rather than an opportunity to improve, 2) Normative comparisons to identify a resident as being behind their peers, and 3) Warning of possible risk to patient care.

Discussion Clinical competency committees (CCCs) usually rely on accumulated data and trends. 

Language: English
Submitted on: Nov 5, 2020
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Accepted on: Jul 11, 2021
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Published on: Sep 2, 2021
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2021 Matthew Kelleher, Benjamin Kinnear, Dana R. Sall, Danielle E. Weber, Bailey DeCoursey, Jennifer Nelson, Melissa Klein, Eric J. Warm, Daniel J. Schumacher, published by Bohn Stafleu van Loghum
This work is licensed under the Creative Commons Attribution 4.0 License.