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
Bibliographic record
Abstract
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 ...
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".