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01.02.2014 | Ausgabe 2/2014

World Journal of Surgery 2/2014

An Objective Evaluation of a Multi-Component, Competitive, Selection Process for Admitting Surgeons into Higher Surgical Training in a National Setting

Zeitschrift:
World Journal of Surgery > Ausgabe 2/2014
Autoren:
Anthony G. Gallagher, Gerald C. O’Sullivan, Paul C. Neary, Sean M. Carroll, Gerald Leonard, Brendan P. Bunting, Oscar Traynor
Wichtige Hinweise
This paper was presented by AG Gallagher at the European Surgical Association meeting, in Hamburg, Germany (5 May 2012).

Abstract

Background

Changing work practices make it imperative that surgery selects candidates for training who demonstrate the spectrum of abilities that best facilitate learning and development of attributes that, by the end of their training, approximate the characteristics of a consultant surgeon.

Aims

The aim of our study was to determine the relative merits of components of a program used for competitive selection of trainees into higher surgical training (HST) in general surgery.

Methods

Applicants (N = 98, males 69, mean age 31 years [range 29–40]) to the Royal College of Surgeons in Ireland program for HST in general surgery between 2006 and 2008 were assessed. Clinical, basic surgical training, logbook, research performance, and reference scores were evaluated. A total of 51 candidates were shortlisted and completed a further objective assessment of their technical skills and interview performances.

Results

Shortlisted candidates performed better (p < 0.003) on all assessed parameters. Compared with candidates who were not selected for HST, those who were selected (N = 31) significantly outperformed on individual assessments and overall (p < 0.0001). Logistic regression analysis showed that clinical, technical skills, and research assessments, but not interview, predicted (92.2 %) HST selection outcomes.

Conclusions

Candidates selected for the national HST program in Ireland consistently outperformed those who were not. The assessments reliably and consistently distinguished between candidates, and all of the assessed parameters (except interview) contributed to a highly predictive selection model. This is the largest reported dataset from an objective, transparent, and fair assessment program for selection of the next generation of surgeons.

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