A closer look at the role of apology in error disclosure: a simulation study
Notice bibliographique
Résumé
Background: The importance of open communication following harmful medical errors is widely accepted including the role of authentic apology. Yet, disclosure conversations remain difficult for clinicians and offering an authentic apology is challenging. Purpose: To better understand how clinicians can improve disclosures and apologies by using simulation to observe the approach clinicians use in the initial disclosure, where and when apologies occur within these conversations, what content apologies are linked with, who apologizes, and how apologies differ by their timing within the overall disclosure conversation. Methods: Forty-nine simulations of physician-nurse teams from the U.S. and Canada were videotaped planning and disclosing either a medical or surgical error to a patient-actress. Data from the disclosure portions were coded and analyzed using Atlas-Ti to describe the communication approach clinicians use when disclosing errors and the occurrence and timing of apologies within those disclosures. Results: Ninety-eight clinicians participated: 38 MD-RN teams from the U.S. and 11 from Canada. Of the 49 total simulated error disclosures, 30 involved medical teams disclosing an insulin overdose; 19 were surgical teams disclosing a lost specimen. The average length of the error disclosure conversations was 9.8 minutes (range = 6.1-14.2 min) and tended to follow a similar roadmap. On average, teams offered 2-3 apologies per disclosure (range = 0-9). These apologies occurred at all points during the disclosures and were offered by both physician and nurse participants. Discussion: Clinicians approached the initial disclosure conversations by addressing nine topics in a relatively consistent order. Apologies occurred throughout the disclosures. With opening comments, clinicians apologized to foreshadow bad news; with closing comments, they linked their remorse to broader professional and organizational goals around patient safety and transparency. Within the disclosure, clinicians sometimes linked the apology to their own emotional experience. More frequently, they linked apologies to the patient's emotional response, which may be more effective to ensure that patients hear that the clinicians' remorse is linked to patient suffering rather than clinician discomfort. To improve these difficult discussions, training materials and guidelines for communicating with patients after harm should reflect the complex role that apologies play.
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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 | 0,000 |
| 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) | 0,000 | 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 ».