Grindrod Kelly, Morris Katherine, Killeen Rosemary
School of Pharmacy, University of Waterloo, Waterloo N2L 3G1, Canada.
Information and Data Management, Ontario College of Pharmacists, Toronto M5R 2R4, Canada.
Pharmacy (Basel). 2020 Feb 24;8(1):26. doi: 10.3390/pharmacy8010026.
A computer-based education platform was developed using a theory-based approach to help Canadian pharmacy professionals adopt their full scope of practice. Data from the platform were used to identify factors that impacted user performance and engagement. A de-identified dataset included response data for 21 unique modules, including quiz responses and self-reflection questions. Outcome measures included user performance (mean quiz score) and engagement (completion rate for attempted modules). Analysis of variance (ANOVA), multivariate regression modelling, and machine learning cluster analysis were used to analyze the data. Of the 5290 users, 68% were pharmacists, 11% were technicians, 13% were pharmacy students, and 8% were pharmacy technician students. Four clusters were identified separately for pharmacists and technicians. Clusters with the higher performance and engagement tended to have more users practicing in community pharmacies while the lower performing clusters tended have more internationally trained users. In the regression modelling, pharmacists performed better than technicians and students while students were more engaged (p < 0.0001). Further, internationally trained pharmacists had slightly lower scores but similar engagement compared to domestically trained pharmacists (p < 0.0001). Users demonstrated higher performance on modules related to scope of practice than on clinical topics, and were most engaged with topics directly impacting daily practice such as influenza vaccinations and new and emerging subjects such as cannabis. The cluster analysis suggests that performance and engagement with a computer-based educational platform in pharmacy may be more related to place of practice than to personal demographic factors such as age or gender.
一个基于计算机的教育平台采用基于理论的方法开发，以帮助加拿大药学专业人员充分发挥其执业范围。该平台的数据用于识别影响用户表现和参与度的因素。一个去识别化的数据集包括21个独特模块的响应数据，包括测验答案和自我反思问题。结果指标包括用户表现（平均测验分数）和参与度（尝试模块的完成率）。使用方差分析（ANOVA）、多元回归建模和机器学习聚类分析来分析数据。在5290名用户中，68%是药剂师，11%是技术员，13%是药学专业学生，8%是药学技术员学生。分别为药剂师和技术员识别出四个聚类。表现和参与度较高的聚类往往有更多在社区药房执业的用户，而表现较低的聚类往往有更多受过国际培训的用户。在回归建模中，药剂师的表现优于技术员和学生，而学生的参与度更高（p < 0.0001）。此外，与国内培训的药剂师相比，受过国际培训的药剂师得分略低，但参与度相似（p < 0.0001）。用户在与执业范围相关的模块上的表现高于临床主题模块，并且对直接影响日常实践的主题（如流感疫苗接种）以及大麻等新出现的主题参与度最高。聚类分析表明，药学领域基于计算机的教育平台的表现和参与度可能与执业地点的关系更大，而不是与年龄或性别等个人人口统计学因素有关。
