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MP73-06 IMPACT OF SURGICAL COACHING ON FACULTY TEACHING SKILLS AND TRAINEE LEARNING EXPERIENCE

2024· article· en· W4394800762 on OpenAlexaboutno aff
Hailey Silverii, Nicolás Fernández, Jennifer Ahn, Maya Gopalan, Apeksha Gupta, Thomas S. Lendvay, Kathleen Kieran, Byron D. Joyner, Margarett Shnorhavorian, Mark P. Cain, Paul A. Merguerian

Bibliographic record

VenueThe Journal of Urology · 2024
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsnot available
Fundersnot available
KeywordsCoachingPsychologyMedical educationQuarter (Canadian coin)Mathematics educationMedicinePsychotherapist

Abstract

fetched live from OpenAlex

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 ...

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.668
Threshold uncertainty score0.473

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.6680.221

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.

Opus teacher head0.018
GPT teacher head0.393
Teacher spread0.375 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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