Literature DB >> 27567113

Predicting surgical skill acquisition in preclinical medical students.

Allison N Martin1, Yinin Hu1, Ivy A Le1, Kendall D Brooks1, Adela Mahmutovic1, Joanna Choi1, Helen Kim1, Sara K Rasmussen2.   

Abstract

BACKGROUND: The purpose of this study was to identify factors that predict medical student success in acquiring invasive procedural skills. We hypothesized that students with interest in surgery and with prior procedural experience would have higher rates of success.
METHODS: Preclinical students were enrolled in a simulation course comprised of suturing, intubation, and central venous catheterization. Students completed surveys to describe demographics, specialty interest area, prior experience, and confidence. Using linear regression, variables predictive of proficiency were identified.
RESULTS: Forty-five participants completed the course. Under univariate analysis, composite pretest score was inversely associated with confidence (P = .039). Under multivariable analysis, female gender was associated with higher pretest suturing score (P = .016). Male gender (P = .029) and high confidence (P = .021) were associated with greater improvement in suturing.
CONCLUSIONS: Among novices, higher confidence can predict lower baseline technical proficiency. Although females had higher pretest suturing scores, high confidence and male gender were associated with the greatest degree of improvement.
Copyright © 2016 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Invasive skills; Medical education; Medical students; Preclinical; Skill acquisition; Surgical simulation

Mesh:

Year:  2016        PMID: 27567113     DOI: 10.1016/j.amjsurg.2016.06.024

Source DB:  PubMed          Journal:  Am J Surg        ISSN: 0002-9610            Impact factor:   2.565


  3 in total

1.  A machine learning approach to predict surgical learning curves.

Authors:  Yuanyuan Gao; Uwe Kruger; Xavier Intes; Steven Schwaitzberg; Suvranu De
Journal:  Surgery       Date:  2019-11-18       Impact factor: 3.982

2.  Gender benefit in laparoscopic surgical performance using a 3D-display system: data from a randomized cross-over trial.

Authors:  Jana Busshoff; Rabi R Datta; Thomas Bruns; Robert Kleinert; Bernd Morgenstern; David Pfister; Costanza Chiapponi; Hans F Fuchs; Michael Thomas; Caroline Gietzelt; Andrea Hedergott; Desdemona Möller; Martin Hellmich; Christiane J Bruns; Dirk L Stippel; Roger Wahba
Journal:  Surg Endosc       Date:  2021-11-08       Impact factor: 3.453

3.  Development of a short and universal learning self-efficacy scale for clinical skills.

Authors:  Yi-No Kang; Chun-Hao Chang; Chih-Chin Kao; Chien-Yu Chen; Chien-Chih Wu
Journal:  PLoS One       Date:  2019-01-07       Impact factor: 3.240

  3 in total

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