Patterns of interaction during rounds: implications for work‐based learning
Notice bibliographique
Résumé
OBJECTIVES In-patient rounds are a major educational and patient care-related activity in teaching hospitals. This exploratory study was conducted to gain better understanding of team interactions during rounds and to assess student and resident perceptions of the utility of this activity. METHODS Data were collected by a non-participant observer using a novel, personal digital assistant (PDA)-based data collection system. Medical students and residents completed surveys related to the utility of rounds for patient care, education and ward administration. Analyses included descriptive and correlational statistics and the use of social network analysis to describe and measure patterns of interaction. RESULTS Eighteen different rounds were observed. On average, rounds were 106 minutes long and included discussion of 22.1 patients. Three different patterns of verbal interaction were observed. In most cases, the attending physician was most talkative and many students and residents spoke infrequently. More time was devoted to patients discussed earlier in the round, regardless of diagnosis. Observed teaching was primarily factual and teacher-centred. Attending physician-dominated sessions were rated more highly for educational utility than those that were more interactive. CONCLUSIONS In-patient rounds are an example of an opportunity for powerful work-based learning. In this study, we used a novel method of observational data collection and analysis to examine this activity and found that it may not always live up to its educational potential. Rounds are time-consuming and are generally dominated by the attending physician. Individuals who are not directly involved in a case are often minimally involved. Participants felt that rounds were most useful for patient care and, contrary to expectations, students and residents viewed attending physician-dominated sessions as more educational. To improve the educational impact of rounds, the order of patient discussion should be planned to highlight specific teaching points, preceptors (teaching staff) should ensure that all team members are actively engaged in the process and learning should be made explicit.
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 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| 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) | 0,001 | 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 ».