Natural frequency tree- versus conditional probability formula-based training for medical students' estimation of screening test predictive values: a randomized controlled trial.

Autor: Kim S; Institute of Health Policy and Management, Seoul National University Medical Research Center, Seoul, South Korea., Kim S; Institute of Health Policy and Management, Seoul National University Medical Research Center, Seoul, South Korea., Choi YJ; Department of Social and Preventive Medicine, Hallym University College of Medicine, Chuncheon, South Korea. ychoi@hallym.ac.kr.; Institute of Social Medicine, Hallym University College of Medicine, Chuncheon, South Korea. ychoi@hallym.ac.kr., Do YK; Institute of Health Policy and Management, Seoul National University Medical Research Center, Seoul, South Korea. ykdo89@snu.ac.kr.; Department of Health Policy and Management, Seoul National University College of Medicine, Seoul, South Korea. ykdo89@snu.ac.kr.
Jazyk: angličtina
Zdroj: BMC medical education [BMC Med Educ] 2024 Oct 24; Vol. 24 (1), pp. 1207. Date of Electronic Publication: 2024 Oct 24.
DOI: 10.1186/s12909-024-06209-0
Abstrakt: Background: Medical students and professionals often struggle to understand medical test results, which can lead to poor medical decisions. Natural frequency tree-based training (NF-TT) has been suggested to help people correctly estimate the predictive value of medical tests. We aimed to compare the effectiveness of NF-TT with conventional conditional probability formula-based training (CP-FT) and investigate student variables that may influence NF-TT's effectiveness.
Methods: We conducted a parallel group randomized controlled trial of NF-TT vs. CP-FT in two medical schools in South Korea (a 1:1 allocation ratio). Participants were randomly assigned to watch either NF-TT or CP-FT video at individual computer stations. NF-TT video showed how to translate relevant probabilistic information into natural frequencies using a tree structure to estimate the predictive values of screening tests. CP-FT video showed how to plug the same information into a mathematical formula to calculate predictive values. Both videos were 15 min long. The primary outcome was the accuracy in estimating the predictive value of screening tests assessed using multiple-choice questions at baseline, post-intervention (i.e., immediately after training), and one-month follow-up. The secondary outcome was the accuracy of conditional probabilistic reasoning in non-medical contexts, also assessed using multiple-choice questions, but only at follow-up as a measure of transfer of learning. 231 medical students completed their participation.
Results: Overall, NF-TT was not more effective than CP-FT in improving the predictive value estimation accuracy at post-intervention (NF-TT: 87.13%, CP-FT: 86.03%, p = .86) and follow-up (NF-TT: 72.39%, CP-FT: 68.10%, p = .40) and facilitating transfer of training (NF-TT: 75.54%, CP-FT: 71.43%, p = .41). However, for participants without relevant prior training, NF-TT was more effective than CP-FT in improving estimation accuracy at follow-up (NF-TT: 74.86%, CP-FT: 58.71%, p = .02) and facilitating transfer of learning (NF-TT: 82.86%, CP-FT: 66.13%, p = .04).
Conclusions: Introducing NF-TT early in the medical school curriculum, before students are exposed to a pervasive conditional probability formula-based approach, would offer the greatest benefit.
Trial Registration: Korea Disease Control and Prevention Agency Clinical Research Information Service KCT0004246 (the date of first trial registration: 27/08/2019). The full trial protocol can be accessed at https://cris.nih.go.kr/cris/search/detailSearch.do?seq=15616&search_page=L .
(© 2024. The Author(s).)
Databáze: MEDLINE
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