Breaking Bad: An Assessment of Ophthalmologists' Interpersonal Skills and Training on Delivering Bad News
Notice bibliographique
Résumé
Purpose This article aims to assess ophthalmologists' practice patterns, experiences, and self-perceived skills when delivering bad news to patients and to compare this to patients' experience and preferences in receiving bad news from ophthalmologists. Design/Methods This is a prospective cross-sectional survey study of two populations: (1) Attending ophthalmologists and current ophthalmologists-in-training (N = 202) at accredited ophthalmology residency programs in the United States and Canada. (2) Patients (N = 151) 18 years of age and older at a single academic center who had received bad news from their ophthalmologist. An e-mail was sent to ophthalmology department chairs and resident program directors requesting that they distribute an online survey to their faculty, fellows, and residents. Patients were recruited from the clinics at an academic center and completed a self-administered survey before their scheduled appointments. Both populations were surveyed on their experience in breaking and receiving bad news, respectively. Questions were rated on a standard five-point Likert scale, and mean score was calculated for statistical comparison. The primary outcome variable was the quantitative rating (Likert scale 1–5) of physicians' communication skills when delivering bad news from physicians and patients' responses. Results Patients rated their physicians higher than physicians rated themselves with regard to ability to deliver bad news (mean score of 4.23 vs. 3.48, p < 0.01). Multivariate analysis showed frequent delivery of bad news (mean score of 3.66 for once per day, 3.53 for per week, 3.40 for once per month, and 3.22 for once per year, linear trend; p = 0.004) and years of practice were associated with better self-perceived ability to deliver bad news (mean score of 3.75 for ≥15 years, 3.48 for <15 years, and 3.30 for residents/fellows, linear trend; p < 0.001). Having received formal training in breaking bad news was associated with better perceived ability score, yet not statistically significant (3.51 vs. 3.39, p = 0.31). Most patients (97.5%) and physicians (92.1%) believe delivering bad news can be taught. Conclusion Physicians and patients agree that skills of delivering bad news can be learned. Patients are less critical of their physicians' ability to deliver bad news than physicians are themselves. Further study of best methods to deliver bad news is clearly indicated for the field of ophthalmology.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 source (Gemma direct ou Codex distillé), 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 ».