“To err is human”, to recover is a must. Residents and Fellows’ perception on error recovery training in surgical specialties
Notice bibliographique
Résumé
BACKGROUND: Medical error is the third leading cause of death in the United States of America. Particularly in the context of surgery, error is a complex matter, in part due to the variety of contributing factors, and the diversity of potential outcomes. To date, emphasis has been placed on strategies for error prevention, however, total error eradication remains an unrealistic target. “To err is human,” and errors can be committed by anyone - trainees and experienced faculty. In the context of surgery, technical errors do occur in the operating room (OR), and these errors must be appropriately recognized and managed to ensure patient safety. Hence, attention should focus not only on decreasing error incidence but also on teaching and learning how to best manage and recover from the inevitable occurrence of errors within a given surgery. Error recovery is an essential skill to attaining surgical competency that may be underrepresented, may not be explicitly taught, and may not be appropriately assessed during surgical training.OBJECTIVE: The objective of this thesis was to explore surgical trainees’ experiences and perceptions of error recovery in surgical procedures. To better understand trainees’ perspectives, two descriptive studies were conducted; one relying on survey methodology, and one relying on semi-structured interviews.METHOD: For the first study, an online survey was sent to surgical trainees in the United States and Canada. It was composed of Likert-scale items, yes/no questions, and open-ended questions. For the second study, semi-structured interviews. Purposive and snowball samplings were used to recruit residents and fellows differing in postgraduate-level and surgical specialty. Interviews were transcribed and a qualitative descriptive approach was used for analysis and data was coded inductively. RESULTS: 206 surveys were completed. Overall, 99% (n=203) agreed or strongly agreed that error recovery is an important competency for future practice. While 83% (n=170) feel confident recovering from “minor” errors, only 34% (n=68) feel confident that they could recover from “major” errors that are likely to have serious consequences on patient safety. Overall, residents do not consider that they have adequate training in error recovery, with only 37% (n=72) felt they were adequately trained to recover from major errors. It was also mentioned “The quality of learning regarding error recovery depends entirely on the attending.” A total of 15 residents and fellows were interviewed. When exploring the importance of error recovery for the trainees, competency and safety emerged as main themes, with error recovery being considered as an indicator of overall surgical competency. Factors that influence error recovery training in the OR were grouped under four major themes: supervision, self, surgical context, and situation safeness. Most of the factors were related to "supervision" in the OR – in other words, the attending appears to be one of the determining factors in whether or not a trainee receives error recovery training. Factors related to the "self" reflected residents' feelings and competencies. "Surgical context" embedded factors related to the procedure and its technical challenge related to the patient’s comorbidity. “Situation safeness” was identified as a transversal theme, describing factors balancing between the patient safety and the learning benefits of error recovery training. CONCLUSION: Error recovery is a skill that is valued by surgical trainees. Participants report they are not receiving adequate training opportunities to learn how to recover from technical errors in the OR. Opportunities are variable, informal, and attending-dependent. Focusing on how to maximize the opportunities for learning by attending to factors related to “supervision”, “self”, “surgical context” and the “situation safeness”, could provide suggestions for improving learning contexts to support error recovery learning
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 | 0,008 | 0,030 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,000 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,002 |
| 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 ».