Accuracy of portrayal by standardized patients: Results from four OSCE stations conducted for high stakes examinations
Notice bibliographique
Résumé
BACKGROUND: The reliability in Objective Structured Clinical Exams (OSCEs) is based on variance introduced due to examiners, stations, items, standardized patients (SP), and the interaction of one or more of these items with the candidates. The impact of SPs on the reliability has not been well studied. Accordingly, the main purpose of the present study was to assess the accuracy of portrayal by standardized patients. METHODS: Four stations from a ten station high-stakes OSCE were selected for video recording. Due to the large number of candidates to be evaluated, the OSCE was administered using four assessment tracks. Four SPs were trained for each case (n = 16). Two physician assessors were trained to assess the accuracy of SP portrayal using a station-specific instrument based on the station guidelines. For the items with disagreement a third physician was asked to review and the mode was used for analysis. Each instrument included case-specific items on verbal and physical portrayal using a 3-point rating scale ("yes", "yes, but" and "not done"). The physician assessors also scored each SP on their overall performance based on a 5-item anchored global rating scale ("very poor", "poor", "ok", "good", and "very good"). SPs at location 1 were trained by one trainer and SPs at location 2 had another trainer. All SPs were employed in a high-stakes OSCE for at least the second time. RESULTS: The reliability of rating scores ranged from Cronbach's alpha of .40 to .74. Verbal portrayal by SPs did not significantly differ for most items; however, the facial expressions of the SPs differed significantly (p < .05). An emergency management station that depended heavily on SPs physical presentation and facial expressions differed between all four SPs trained for that station. CONCLUSIONS: Variation of trained SP portrayal of the same station across different tracks and at different times in OSCE may contribute substantial error to OSCE assessments. The training of SPs should be strengthened and constantly monitored during the exam to ensure that the examinees' scores are a true reflection of their competency and devoid of exam errors.
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 | 0,001 | 0,114 |
| 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 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».