“It’s Complicated”: Understanding the Relationships Between Checklists, Rating Scales, and Written Comments in Workplace-Based Assessments
Notice bibliographique
Résumé
Purpose: The shift towards competency-based medical education in postgraduate medical training has transformed the way competence is taught and measured within medical education. Competency-based medical education is built upon the belief that competence can be assessed using frequent and meaningful assessments, many of which consist of both quantitative and qualitative evaluations. In order to understand associations between various quantitative and qualitative evaluations used in workplace-based assessments, this study aimed to explore the relationships that exist between assessors’ checklist scores, ratings, and written comments. Method: Data from the McMaster Modular Assessment Program (McMAP)1 were collected and analyzed using an explanatory mixed-methods design. McMAP was designed to assess the CanMEDS roles of postgraduates specializing in emergency medicine using checklists, rating scales, and written comments for both task-specific and global appraisals of competence. These workplace-based assessment checklist and rating scale scores were analyzed using regression analyses. Narrative comments, corresponding with the aforementioned numeric scores, were rated by a content expert using a modified version of the Completed Clinical Evaluation Report Rating2 and used as predictor variables in the regression analyses. The written comments appearing in the workplace-based assessment were also independently analyzed by two of the authors using content analysis. Results: Communicator and collaborator workplace-based assessments from 342 McMAP evaluations of postgraduate year (PGY) 1 and PGY2 residents were analyzed using logistic regression and content analysis. Results from the two regression models indicated that the task-specific ratings provided by faculty assessors were significant in determining whether the “done, but needs attention” checklist category was used. Furthermore, the “done, but needs attention” checklist category was most significant in determining whether a written comment, mentioning specific strengths and weaknesses, would appear in the McMAP assessment. Subsequent analysis of the qualitative comments suggested meaningful differences in the type of written feedback provided in workplace-based assessments. Our analysis supports the notion of a hidden code3 used by assessors to communicate levels of competence. Conclusions: This study highlights some of the relationships that exist between checklists, rating scales, and written comments. As more institutions transition toward competency-based medical education, it becomes imperative that relationships among different forms of assessment are known in order to develop and implement comprehensive assessment programs. Findings from this study suggest that task and global ratings are differentially related to checklists, which has broader implications for the development of assessment tools. Furthermore, the presence of a hidden code creates challenges when interpreting information obtained from workplace-based assessments.
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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 | 0,268 | 0,734 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,002 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,008 | 0,007 |
| Études des sciences et des technologies | 0,002 | 0,009 |
| Communication savante | 0,009 | 0,023 |
| Science ouverte | 0,003 | 0,007 |
| Intégrité de la recherche | 0,002 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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; l’étiquette directe de Gemma et le classifieur distillé Codex s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».