Dataset Information


Meta-analysis on the effectiveness of team-based learning on medical education in China.

ABSTRACT: Team-based learning (TBL) has been adopted as a new medical pedagogical approach in China. However, there are no studies or reviews summarizing the effectiveness of TBL on medical education. This study aims to obtain an overall estimation of the effectiveness of TBL on outcomes of theoretical teaching of medical education in China.We retrieved the studies from inception through December, 2015. Chinese National Knowledge Infrastructure, Chinese Biomedical Literature Database, Chinese Wanfang Database, Chinese Scientific Journal Database, PubMed, EMBASE and Cochrane Database were searched. The quality of included studies was assessed by the Newcastle-Ottawa scale. Standardized mean difference (SMD) was applied for the estimation of the pooled effects. Heterogeneity assumption was detected by I2 statistics, and was further explored by meta-regression analysis.A total of 13 articles including 1545 participants eventually entered into the meta-analysis. The quality scores of these studies ranged from 6 to 10. Altogether, TBL significantly increased students' theoretical examination scores when compared with lecture-based learning (LBL) (SMD?=?2.46, 95% CI: 1.53-3.40). Additionally, TBL significantly increased students' learning attitude (SMD?=?3.23, 95% CI: 2.27-4.20), and learning skill (SMD?=?2.70, 95% CI: 1.33-4.07). The meta-regression results showed that randomization, education classification and gender diversity were the factors that caused heterogeneity.TBL in theoretical teaching of medical education seems to be more effective than LBL in improving the knowledge, attitude and skill of students in China, providing evidence for the implement of TBL in medical education in China. The medical schools should implement TBL with the consideration on the practical teaching situations such as students' education level.


PROVIDER: S-EPMC5894173 | BioStudies | 2018-01-01

REPOSITORIES: biostudies

Similar Datasets

2019-01-01 | S-EPMC6664710 | BioStudies
2020-01-01 | S-EPMC7220526 | BioStudies
2020-01-01 | S-EPMC7451570 | BioStudies
1000-01-01 | S-EPMC6282484 | BioStudies
2019-01-01 | S-EPMC6457546 | BioStudies
2015-01-01 | S-EPMC4675422 | BioStudies
2016-01-01 | S-EPMC6440433 | BioStudies
1000-01-01 | S-EPMC5134940 | BioStudies
2007-01-01 | S-EPMC1913552 | BioStudies
1000-01-01 | S-EPMC4040881 | BioStudies