Peer assessment of professionalism in undergraduate medical education
Notice bibliographique
Résumé
BACKGROUND: Fostering professional behaviour has become increasingly important in medical education and non-traditional approaches to assessment of professionalism may offer a more holistic representation of students' professional behaviour development. Emerging evidence suggests peer assessment may offer potential as an alternative method of professionalism assessment. We introduced peer assessment of professionalism in pre-clerkship phases of undergraduate medical education curriculum at our institution and evaluated suitability of adopting a professional behaviour scale for longitudinal tracking of student development, and student comfort and acceptance of peer assessment. METHODS: Peer assessment was introduced using a validated professional behaviours scale. Students conducted repeated, longitudinal assessments of their peers from small-group, clinical skills learning activities. An electronic assessment system was used to collect peer assessments, collate and provide reports to students. Student opinions of peer assessment were initially surveyed before introducing the process, confirmatory analyses were conducted of the adopted scale, and students were surveyed to explore satisfaction with the peer assessment process. RESULTS: Students across all phases of the curriculum were initially supportive of anonymous peer assessment using small-group learning sessions. Peer scores showed improvement over time, however the magnitude of increase was limited by ceiling effects attributed to the adopted scale. Students agreed that the professional behaviours scale was easy to use and understand, however a majority disagreed that peer assessment improved their understanding of professionalism or was a useful learning experience. CONCLUSIONS: Peer assessment of professional behaviours does expose students to the process of assessing one's peers, however the value of such processes at early stages of medical education may not be fully recognized nor appreciated by students. Electronic means for administering peer assessment is feasible for collecting and reporting peer feedback. Improvement in peer assessed scores was observed over time, however student opinions of the educational value were mixed and indeterminate.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,039 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 tête enseignante, 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 ».