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Generative Retrieval Does Not Improve Long-Term Retention of Regional Anesthesia Ultrasound Anatomy in Unengaged Learners.
J Educ Perioper Med ; 21(2): E623, 2019.
Article em En | MEDLINE | ID: mdl-31988984
ABSTRACT

BACKGROUND:

Ultrasound-guided regional anesthesia is increasingly used in the perioperative period but performance requires a mastery of regional ultrasound anatomy. We aimed to study whether the use of generative retrieval to learn ultrasound anatomy would improve long-term recall.

METHODS:

Fourth-year medical students without prior training in ultrasound techniques were randomized into standard practice (SP) and generative retrieval (GR) groups. An initial pre-test consisted of 74 regional anesthesia ultrasound images testing common anatomic structures. During the study/learning session, GR participants were required to verbally identify an unlabeled anatomical structure within 10 seconds of the ultrasound image appearing on the screen. A labeled image of the structure was then shown to the GR participant for 5 seconds. SP participants viewed the same ultrasound images labeled with the correct anatomical structure for 15 seconds. Retention was tested at 1 week and 1 month following the study session. Participants completed a satisfaction survey after each session.

RESULTS:

Forty-five medical students were enrolled with forty included in the analysis. There was no statistically significant difference in baseline scores (GR = 11.5 ± 4.9; SP = 11.2 ± 6.2; P = 0.84). There was no difference in scores at both the 1-week (SP = 54.5 ± 13.3; GR = 53.9 ± 10.5; P = 0.88) and 1-month (SP = 54.0 ± 14.5; GR = 50.7 ± 11.1; P = 0.42) time points. There was no statistically significant difference in learner satisfaction metrics between the groups.

CONCLUSIONS:

The use of generative retrieval practice to learn regional anesthesia ultrasound anatomy did not yield significant differences in learning and retention compared with standard learning.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2019 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2019 Tipo de documento: Article