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A secondary data analysis using Bayesian statistics to explore the influence of gender and initial performance on skill acquisition using a laparoscopy simulator

Posen, L. (2020) A secondary data analysis using Bayesian statistics to explore the influence of gender and initial performance on skill acquisition using a laparoscopy simulator.

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Abstract:Background: The aim of simulators in the medical context is to move the critical part of the learning curve, where mistakes and lapses occur, from the patient to the simulator. For this to occur, selecting an optimal training strategy is necessary. For example, a proficiency-based program reduced surgery residents mistakes in their first 10 laparoscopic surgeries (Ahlberg et al., 2007). Unfortunately, current training strategies are not adapted to individual differences, which could improve effectiveness/efficiency by providing an environment for deliberate practice, where improvement occurs through conscious effort (Ericsson, 2004). Exploring individual differences would enable the development of individualized training programs and assessment procedures. Objective: The main objective of the study is to explore how individual differences in gender and initial performance influence skill acquisition on LapSim. The secondary objective was to use a Bayesian approach which compared to a Frequentist approach, should generate more accurate inferences as it produces better model fit for complex data. Methods: Data was acquired by Groenier, Groenier, Miedema, & Broeders (2015) and Groenier, Schraagen, Miedema, & Broeders (2014) who used Frequentist approaches, while the current study used Bayesian. In the longitudinal study, 67 participants completed weekly 30-minute training sessions. For analysis duration and damage count assessed performance of the first 5 sessions as two tasks – grasping and instrumental navigation – were conducted at medium level difficulty. Main Findings: 1) No gender differences were found for speed; however, gender differences were found for accuracy. 2) Initial performance differences were reduced with practice, for both speed and accuracy. 3) For model criticism, using gender as a level had no predictive ability, while initial performance levelling did. As gender showed no predictive ability, it would not be useful for forecasting as it does not provide additional knowledge on how participants perform. 4) For model fit, duration data showed poor fit for all distributions - ExGaussian, Gaussian, and Gamma; this poor fit may create more uncertainty and less precise estimations. Damage count data showed the best fit with a Poisson distribution. Conclusion: No male advantage was found, which is contrary to past research where males hold an advantage for visuospatial tasks. Although females had an advantage for accuracy, it subsided with practice. As differences are not pronounced, we recommend that individualized training programs should not be implemented for gender groups; which goes against Donnon, DesCôteaux, and Violato (2005) who suggested one-on-one training was beneficial for female laparoscopic trainees. Initial performance produces transient performance outcomes, as differences in initial accuracy and speed become less influential as practice occurred. From these findings, we recommend that for assessment of laparoscopic skill, one-time initial testing and screening is inappropriate and should be avoided when selecting potential trainees. Keywords: Minimally Invasive Surgery, Laparoscopy, Simulators, Individual Differences, Gender, Initial Performance, Learning Curves, Skill Acquisition, Multilevel Modelling, Bayesian Analysis
Item Type:Essay (Master)
Faculty:BMS: Behavioural, Management and Social Sciences
Subject:44 medicine, 77 psychology
Programme:Psychology MSc (66604)
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