Building and Exploitation of Learning Curves to Train Radiographer Students in X-Ray CT Image Postprocessing.
J Med Imaging Radiat Sci
; 51(1): 173-181, 2020 03.
Article
em En
| MEDLINE
| ID: mdl-32057745
INTRODUCTION: This study aims to construct learning curves related to the realization of standardized postprocessing by radiographer students and to discuss their exploitation and interest. MATERIALS AND METHODS: This study was carried out in 21 French students in their 3rd year of training. Two postprocessing protocols in CT (#1 traumatic shoulder; #2 petrous bone) were repeated 15 times by each student. Each achievement was timed to obtain overall learning curves. The realization accuracy was also assessed for each student at each repetition. RESULTS: The learning rates for the two protocols are 63% and 56%, respectively. The number of repetitions to reach the reference time for each protocol is 11 and 12, respectively. In both protocols, the standard deviations are significantly reduced and stabilized during repetitions. The mean accuracy progresses more quickly in protocol #1. DISCUSSION: The measured learning rates reflect a rapid learning process for each protocol. The analysis of the standard deviations shows that students have reached a homogeneous level. The average times and accuracies measured during the last repetitions show that the group has reached a high level of performance. Building learning curves helps students measure their progress and motivates them. CONCLUSION: Obtaining learning curves allows trainers/supervisors to qualify the learning difficulty of a task while motivating students/radiographers. The use of learning curves is inline with the competency-based training paradigm.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Interpretação de Imagem Radiográfica Assistida por Computador
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Tomografia Computadorizada por Raios X
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Tecnologia Radiológica
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Competência Clínica
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Curva de Aprendizado
Limite:
Humans
País/Região como assunto:
Europa
Idioma:
En
Revista:
J Med Imaging Radiat Sci
Ano de publicação:
2020
Tipo de documento:
Article
País de afiliação:
França