MP35-18 DEVELOPMENT, IMPLEMENTATION, AND EVALUATION OF A COMPETENCY-BASED DIDACTIC AND SIMULATION-FOCUSED BOOT CAMP FOR INCOMING UROLOGY RESIDENTS: REPORT OF FIRST TWO YEARS
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Résumé
You have accessJournal of UrologySurgical Technology & Simulation: Training & Skills Assessment II (MP35)1 Apr 2019MP35-18 DEVELOPMENT, IMPLEMENTATION, AND EVALUATION OF A COMPETENCY-BASED DIDACTIC AND SIMULATION-FOCUSED BOOT CAMP FOR INCOMING UROLOGY RESIDENTS: REPORT OF FIRST TWO YEARS Yuding Wang*, Jen Hoogenes, Udi Blankstein, Kevin Kim, Roderick Clark, Ali Al-Hashimi, Bobby Shayegan, and Edward Matsumoto Yuding Wang*Yuding Wang* More articles by this author , Jen HoogenesJen Hoogenes More articles by this author , Udi BlanksteinUdi Blankstein More articles by this author , Kevin KimKevin Kim More articles by this author , Roderick ClarkRoderick Clark More articles by this author , Ali Al-HashimiAli Al-Hashimi More articles by this author , Bobby ShayeganBobby Shayegan More articles by this author , and Edward MatsumotoEdward Matsumoto More articles by this author View All Author Informationhttps://doi.org/10.1097/01.JU.0000556008.17548.f3AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVES: The integration of competency-based education into surgical residency programs presents challenges for curricula design. Surgical boot camps have been used to improve the learning process by orienting and preparing new residents. We developed, implemented, and evaluated an intensive didactic and simulation-focused boot camp for first-year urology residents to determine its utility and feasibility for potential integration into our formal competency-based curriculum. We report our experience with two years of implementation of the boot camp. METHODS: For each of the two years, 6 first-year residents from two universities participated in the 2-day boot camp at the beginning of their residency. The boot camp included 11 didactic lectures that covered first-year medical and surgical topics, and 6 simulation sessions that allowed for instruction and deliberate practice with feedback. Participants completed an entrance and exit survey and an identical pre- and post-boot camp 31-item multiple choice questionnaire (MCQ). At the end of day two, participants completed a 6-station objective structured clinical exam (OSCE) followed by a semi-structured group feedback discussion. After the first year, 3 second-year urology residents served as historical controls and completed the identical MCQ and OSCE. The OSCEs were evaluated by senior residents and staff surgeons. RESULTS: The 12 participants represented 8 medical schools, had a mean age of 26, with 9 males and 3 females. Most prior urology experience was as an observer or 2nd assist, with minimal simulation experience. Participants markedly improved on the pre- and post- MCQs (year 1: 62% and 91%, respectively; year 2: 55% and 89%, respectively), whereas the historical controls scored 66%. Participants scored marginally higher than the controls on 4 of the 6 OSCE stations. There were no significant differences in participants’ OSCE scores between years. All participants reported overall higher confidence levels and felt that the curriculum was an excellent preparation for residency. CONCLUSIONS: Our urology boot camp has demonstrated high feasibility and utility. The knowledge and technical skills uptake was established via the MCQ and OSCE results, with participants’ performance at or even above the level of the second-year urology resident historical controls. We aim to further develop our boot camp, implement it annually as part of our competency-based curriculum, and provide a framework that can be used by other urology residency programs. Source of Funding: McMaster University Surgical Associates Hamilton, Canada; London, Canada; Hamilton, Canada© 2019 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 201Issue Supplement 4April 2019Page: e513-e513 Advertisement Copyright & Permissions© 2019 by American Urological Association Education and Research, Inc.MetricsAuthor Information Yuding Wang* More articles by this author Jen Hoogenes More articles by this author Udi Blankstein More articles by this author Kevin Kim More articles by this author Roderick Clark More articles by this author Ali Al-Hashimi More articles by this author Bobby Shayegan More articles by this author Edward Matsumoto 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 | 0,015 | 0,023 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 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) | 0,009 | 0,003 |
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 ».