Characteristics of Surgical Coaching Interventions: A Systematic Review
Notice bibliographique
Résumé
OBJECTIVE: Coaching is increasingly utilized as an educational intervention for performance improvement in surgeons and surgical trainees. Surgical coaching has been utilized across a broad range of specialties, experience levels and outcomes with generally positive results. Coaching interventions are often developed by individual institutions for their own context which has resulted in a heterogenous group of interventions. This review aims to investigate surgical coaching interventions to identify common characteristics that comprise an effective coaching intervention. METHODS: A systematic review was conducted to identify studies investigating surgical coaching interventions up to July 2024. Studies were limited to English language peer-reviewed studies that adequately described the characteristics and outcomes of the surgical coaching intervention. Data on the primary and secondary outcomes, study objective and participants' demographics were also recorded. RESULTS: The search across 4 electronic databases generated 9538 citations. Following screening and review of full text articles 28 studies were included in the review. Surgical coaching interventions were carried out in 8 separate countries with the majority (22/28) in North America. Studies involved between 3 and 107 participants. Coaching interventions were markedly heterogenous, and specific details of the methods used were inconsistently documented. Study length ranged from 1 (9/28) to 14 (1/28) sessions and duration from less than 15 minutes (1/29) to greater than 3 hours (3/28). The most common themes were goal setting (10/28), feedback (7/28) and reflection (7/28). Outcomes were generally positive with 47 of 55 identified outcomes demonstrating benefit from surgical coaching. There were 3 key domains and 13 sub-domains that comprised the majority of coaching interventions. CONCLUSIONS: Surgical coaching has been shown to be a promising intervention that requires more rigorous research to develop the field. We have identified 3 key domains which can be utilized to analyses and develop coaching interventions in the future.
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 | 0,002 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,005 | 0,003 |
| Bibliométrie | 0,001 | 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 | 0,001 |
| 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 ».