Why Are Students Appealing Clerkship Grades? A Multischool Root Cause Analysis
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,022 | 0,050 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,003 |
| Bibliométrie | 0,013 | 0,008 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».