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Record W4387949267 · doi:10.1097/acm.0000000000005414

Why Are Students Appealing Clerkship Grades? A Multischool Root Cause Analysis

2023· article· en· W4387949267 on OpenAlexaff
Judith Brenner, Aubrie Swan Sein, Jonathan M. Amiel, Todd Cassese, Mimoza Meholli, Allison B. Ludwig, Robin K. Ovitsh, Lyuba Konopasek, Lisa Auerbach, Samara Ginzburg

Bibliographic record

VenueAcademic Medicine · 2023
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsColumbia College
Fundersnot available
KeywordsGrading (engineering)Medical educationUnited States Medical Licensing ExaminationMedical schoolMedicineHigher educationFamily medicinePsychologyPolitical science

Abstract

fetched live from OpenAlex

Purpose: As educators in undergraduate medical education, we seek to maximize student learning, grading transparency and fairness, and provide useful information to residency programs that support continued professional development. In recent years medical schools have encountered disruptions to curricular and assessment operations, in part due to the impacts of COVID-191 and the change to a pass/fail-scored United States Medical Licensing Examination (USMLE) Step 1. Medical schools have also developed an increased awareness of long-standing systemic inequities in the grading of nonmajority racial groups.2 Contemporaneously, clerkship grade appeals were becoming noticeable enough to clerkship directors in medicine (CDIM) and psychiatry that national surveys were conducted to begin to quantify the prevalence of these appeals and try to understand reasons for them.3,4 In noting that significant institutional resources were being expended in addressing the present levels of student grade appeals, our group sought to extend the literature by engaging in a systematic analysis of grade appeals across 6 medical schools. Method: Six medical schools (Albert Einstein College of Medicine, Columbia University Vagelos College of Physicians and Surgeons, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Frank H. Netter School of Medicine—Quinnipiac University, The State University of New York Downstate College of Medicine, The City University of New York School of Medicine), including public, private, research-intensive, and primary care-oriented institutions, sought to learn more about grade appeals and systems challenges at our schools. All schools contributed descriptive data regarding processes and criteria for clerkship grade assignments, which were tiered (variations of honors/high pass/pass/fail) at all institutions, and clerkship grade appeal processes. The group examined the central question, “Why are students challenging grading processes/systems or outcomes?” through a modified root cause analysis (RCA).5 Using this modified RCA approach, the authors identified multiple contributing factors including system challenges that potentially lead students to appeal clerkship grades. These factors were mapped to standards/elements from the Liaison Committee on Medical Education (LCME) Data Collection Instrument as a means to structure quality and process improvements in clerkship grade assignments to address the issue of student clerkship grade appeals and system challenges more holistically. Results: Grade appeal reasons fell most commonly into Standard 9 (teaching, supervision, assessment), with reported issues including perceived variability in raters’ use of assessment forms, inconsistencies between mid-clerkship feedback and final grades, and perceptions of the lack of transparency in grade determination. Standard 4 (faculty preparation, productivity, participation, and policies) was another common area for reasons to appeal, related to potential issues with faculty development on how to use assessment or rating forms, or differential faculty assessment training across clinical sites. Standards 3, 5, 6, 8, 10, and 11 also were identified as containing potential reasons for student grade appeals. Additionally, reasons were identified that did not fit into LCME standards but potentially impact grade appeals. These “student factors” included students who are more comfortable self-advocating and negotiating for grades and/or culture of privilege in seeking further justification of grades, as well as national use of grades in residency candidacy decisions. Discussion: We found many similarities in potential reasons for submitting grade appeals and challenging grading systems across institutions. Classifying reasons for grade appeal and system challenges into LCME standards is useful because schools often assign standards and specific elements within standards to individual departments or stakeholders for continuous quality improvement interventions. For example, faculty development-related challenges were identified and, thus, could be targeted for evaluation and improvement. As another example, assessment or grading committees could address challenges related to perceptions of the lack of grading transparency and potentially decide to share some group grading data back with students. Significance: We found that conducting a modified RCA to understand the issues giving rise to grade appeals and using an LCME framework to classify reasons for the grade appeals is a useful approach to identifying specific areas for improvement as well as stakeholders who can help to address them. The LCME framework was an effective way to classify the majority of the reasons for appeals, with “student factors” capturing the rest. With the ultimate goal of creating an optimal learning environment and a fair and equitable assessment process in mind, we believe this methodology can contribute to making improvements to our grading systems that enhance student learning and success.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.022
metaresearch head score (Gemma)0.050
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.978
Threshold uncertainty score0.117

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.050
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0130.008
Science and technology studies0.0020.001
Scholarly communication0.0030.002
Open science0.0020.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.071
GPT teacher head0.436
Teacher spread0.364 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designQualitative
DomainEvaluation
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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