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Board 428 - Research Abstract Development of an Integrated Surgical Skills Course

2013· article· en· W2331707483 on OpenAlexaffabout
Diana Tabak

Bibliographic record

VenueSimulation in Healthcare The Journal of the Society for Simulation in Healthcare · 2013
Typearticle
Languageen
FieldHealth Professions
TopicDental Education, Practice, Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTest (biology)Session (web analytics)Interpersonal communicationMedical educationMedicineMedical physicsPsychologySimulated patientSimulationComputer sciencePhysical therapyCommunication

Abstract

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Introduction/Background Hybrid simulation, combining simulated/standardized patients (SPs) and technical skill training kits (e.g. suture pads attached to the arm) can lead to immediate improvements in surgical residents communication skills while preserving technical proficiency.1 Our goals were: 1) to assess whether learning on hybrid simulations is sustained on a delayed-post intervention retention test; 2) to evaluate the added role of feedback vs simple exposure in leading to improvements and 3) to assess the level of acceptance for SP-based hybrid simulations in a highly technical domain. Methods The study design was a prospective randomized control study, with a delayed post-test to assess retention. Twenty four residents were randomly divided into the Feedback or Exposure groups. All residents were exposed to four, 15-minute SP-based hybrid simulations over five weeks during their regularly scheduled weekly procedural skill laboratory (one hybrid simulation per week), combining a procedural and communication challenge (e.g. wound closure on intoxicated patient). Residents in the Feedback group remained for a further 15- minute, video-prompted feedback session with the SP, focused on their communication and interpersonal skills. Similar feedback was given to each participant in the Exposure group but not until after the post-test assessment was completed. All residents were assessed on a pre-test using two hybrid scenarios and on a delayed post-test using two (new) hybrid scenarios. The post-test occurred two weeks after the last teaching simulation. The residents also completed an exit survey about their experience of the hybrid simulations.` Results Performance was assessed by two independent raters using a communication global rating scale (GRS), a technical skills global rating scale and a combined assessment of technical and communication skills. Raters were blinded to the study hypotheses and group allocation. There were no differences between the two groups at baseline. At post-test, residents in the Feedback group outperformed the Exposure group on the communication GRS (p<.05) and the combined assessment (p<.05). There was a trend toward higher scores on the technical skills GRS, but this difference did not reach significance (p=.16). Analysis of the exit survey revealed that while a subset of the residents reported feeling ill-prepared (37.5%), confused (8%) and frustrated (12.5%) by the simulations, a large subset reported the exposure as enjoyable (70.8%) and valuable (87.5%). Ninety six percent of the residents had previously experienced similar communication challenges in their residency training. Conclusion Improvements in communication skills following SP-based hybrid simulations are maintained at delayed post-test for retention and appear to depend on feedback received from SPs. References 1. Moulton CA, Tabak D, Kneebone R, Nestel D, MacRae H, LeBlanc VR. (2009). Teaching. Communication Skills Using the Integrated Procedural Performance Instrument (IPPI): A Randomized Controlled Trial. American Journal of Surgery, 197, 113-118. D Tabak3, C Moulton1, A Robb3, H MacRae1, D Nestel5, R Kneebone4, V LeBlanc1 1Wilson Centre, Faculty of Medicine, University of Toronto, Ontario, Canada. 2Department of Surgery, University of Toronto, Ontario, Canada. 3Standardized Patient Program, University of Toronto, Ontario, Canada. 4Department of Biosurgery & Surgical Technology, Imperial College London, London UK. 5School of Rural Health, HealthPEER, Monash University, Churchill, Australia. Disclosures None.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.021
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.348
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0210.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0000.000

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 head0.173
GPT teacher head0.572
Teacher spread0.399 · 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 teacher head, 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
Published2013
Admission routes2
Has abstractyes

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