Currently submitted to: JMIR Medical Education
Date Submitted: Mar 1, 2026
Open Peer Review Period: Mar 6, 2026 - May 1, 2026
(currently open for review)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Comparative efficacy of emerging teaching methods in pharmacy education: A network meta-analysis
ABSTRACT
Background:
The efficacies of various teaching methods in improving pharmacy students’ qualities and skills remain unclear.
Objective:
We aimed to compare and rank teaching methods by quantifying information from randomized controlled trials.
Methods:
A systematic literature search of PubMed, Web of Science, China National Knowledge Infrastructure, and Chinese Wanfang Database was performed from the date of inception of the databases to November 2025. Our primary outcomes were the proportion of satisfaction and core competencies measured with survey questionnaires.
Results:
144 trials comprising 18793 students allocated to one of 35 teaching methods were included. Problem-based learning combined with web-based learning (PBL+WBL) has the highest probability of being the best teaching method in improving the proportion of satisfaction (the surface under the cumulative ranking curve [SUCRA]=84.19%) and mastery of knowledge (SUCRA=77.87%) of pharmacy students. Problem-based learning combined with scenario simulation (PBL+SS) was most likely to enhance learning interest (SUCRA=98.39%) and teamwork ability (SUCRA=99.17%) in pharmacy education. In addition, team-based learning (TBL) was the most efficacious in increasing self-learning ability (SUCRA=81.46%), problem-solving ability (SUCRA=94.98%), and theoretical scores (SUCRA=91.92%).
Conclusions:
Our study indicates that emerging teaching methods represented by TBL, PBL+WBL, and PBL+SS are more effective in pharmacy education. Nevertheless, potential publication bias deserves careful consideration. Future research should use larger sample sizes and more rigorous methods to support these findings.
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