Development of a Web-Based Formative Self-Assessment Tool for Physicians to Practice Breaking Bad News (BRADNET)
Notice bibliographique
Résumé
BACKGROUND: Although most physicians in medical settings have to deliver bad news, the skills of delivering bad news to patients have been given insufficient attention. Delivering bad news is a complex communication task that includes verbal and nonverbal skills, the ability to recognize and respond to patients' emotions and the importance of considering the patient's environment such as culture and social status. How bad news is delivered can have consequences that may affect patients, sometimes over the long term. OBJECTIVE: This project aimed to develop a Web-based formative self-assessment tool for physicians to practice delivering bad news to minimize the deleterious effects of poor way of breaking bad news about a disease, whatever the disease. METHODS: BReaking bAD NEws Tool (BRADNET) items were developed by reviewing existing protocols and recommendations for delivering bad news. We also examined instruments for assessing patient-physician communications and conducted semistructured interviews with patients and physicians. From this step, we selected specific themes and then pooled these themes before consensus was achieved on a good practices communication framework list. Items were then created from this list. To ensure that physicians found BRADNET acceptable, understandable, and relevant to their patients' condition, the tool was refined by a working group of clinicians familiar with delivering bad news. The think-aloud approach was used to explore the impact of the items and messages and why and how these messages could change physicians' relations with patients or how to deliver bad news. Finally, formative self-assessment sessions were constructed according to a double perspective of progression: a chronological progression of the disclosure of the bad news and the growing difficulty of items (difficulty concerning the expected level of self-reflection). RESULTS: The good practices communication framework list comprised 70 specific issues related to breaking bad news pooled into 8 main domains: opening, preparing for the delivery of bad news, communication techniques, consultation content, attention, physician emotional management, shared decision making, and the relationship between the physician and the medical team. After constructing the items from this list, the items were extensively refined to make them more useful to the target audience, and one item was added. BRADNET contains 71 items, each including a question, response options, and a corresponding message, which were divided into 8 domains and assessed with 12 self-assessment sessions. The BRADNET Web-based platform was developed according to the cognitive load theory and the cognitive theory of multimedia learning. CONCLUSIONS: The objective of this Web-based assessment tool was to create a "space" for reflection. It contained items leading to self-reflection and messages that introduced recommended communication behaviors. Our approach was innovative as it provided an inexpensive distance-learning self-assessment tool that was manageable and less time-consuming for physicians with often overwhelming schedules.
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 | 0,002 |
| 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 | 0,001 | 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 ».