MP73-06 IMPACT OF SURGICAL COACHING ON FACULTY TEACHING SKILLS AND TRAINEE LEARNING EXPERIENCE
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Résumé
You have accessJournal of UrologySurgical Technology & Simulation: Training & Skills Assessment (MP73)1 May 2024MP73-06 IMPACT OF SURGICAL COACHING ON FACULTY TEACHING SKILLS AND TRAINEE LEARNING EXPERIENCE Hailey Silverii, Nicolas Fernandez, Jennifer Ahn, Maya Gopalan, Apeksha Gupta, Thomas Lendvay, Kathleen Kieran, Byron Joyner, Margarett Shnorhavorian, Mark Cain, and Paul Merguerian Hailey SilveriiHailey Silverii , Nicolas FernandezNicolas Fernandez , Jennifer AhnJennifer Ahn , Maya GopalanMaya Gopalan , Apeksha GuptaApeksha Gupta , Thomas LendvayThomas Lendvay , Kathleen KieranKathleen Kieran , Byron JoynerByron Joyner , Margarett ShnorhavorianMargarett Shnorhavorian , Mark CainMark Cain , and Paul MerguerianPaul Merguerian View All Author Informationhttps://doi.org/10.1097/01.JU.0001009564.26544.1c.06AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Surgical coaching has been shown to improve surgeons' teaching abilities; however, such a coaching model has not been formally studied within pediatric urology. In this study, we implemented an expert coaching model focused on faculty development and aimed to assess the impact of the model on surgeon teaching abilities and the trainee experience. METHODS: Survey data were collected via REDCap-hosted anonymous surveys including de novo designed questions for trainee environment collected by operating room staff (360 Review), Zwisch scale (ZS) collected by coach, coachee, and trainee to assess trainee autonomy, and the Systematic Evaluation of Teaching Qualities (SETQ) completed by trainees following each case in the model (see Figure 1). The survey data from quarter 1 (July 1, 2023 -September 30, 2023) were analyzed descriptively. RESULTS: Fifteen cases were included within the quarter: six open cases and nine robotic cases. Trainee level ranged from PGY 2-PGY 7. There was at least 1 trainee present for all cases, and 2 trainees present for 46.7% of cases . OR staff response rate for 360 Review surveys was 48.0%. Trainee response rate for assessments (SETQ, ZS) was 54.5%, while coach and coachee response rates were 100% and 93.3% respectively (ZS). 360 Review surveys suggest an overwhelmingly positive and engaging environment for trainees (Figure 2). ZAS aligned only 28.6% between coach-coachee, and 50% of the time between coach-trainee and coachee-trainee dyads. ZAS varied per case but trended upwards with advanced training. SETQ scores varied between coachees; however, median scores were consistently above 4 for all domains (on a scale of 1-5). CONCLUSIONS: Early data from a newly implemented coaching program suggest that the trainee environment is positive, though perception of autonomy differs between stakeholders. Teaching evaluations are overall positive but further data are needed to assess whether improvement in coachee SETQ scores is seen with further coaching. The faculty goal-setting coaching model may be applicable, and beneficial to primary resident training paradigms. Download PPTDownload PPT Source of Funding: Training and Human Performance Research Grant. Intuitive Foundation. Grant Awarded for 7/2023-6/2024 © 2024 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 211Issue 5SMay 2024Page: e1183 Advertisement Copyright & Permissions© 2024 by American Urological Association Education and Research, Inc.Metrics Author Information Hailey Silverii More articles by this author Nicolas Fernandez More articles by this author Jennifer Ahn More articles by this author Maya Gopalan More articles by this author Apeksha Gupta More articles by this author Thomas Lendvay More articles by this author Kathleen Kieran More articles by this author Byron Joyner More articles by this author Margarett Shnorhavorian More articles by this author Mark Cain More articles by this author Paul Merguerian More articles by this author Expand All Advertisement PDF downloadLoading ...
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 | 0,007 |
| 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 | 0,003 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,668 | 0,221 |
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 ».