Show simple item record Letsoalo, M. E Olivier, E. 2023-10-25T13:42:47Z 2023-10-25T13:42:47Z 2023
dc.identifier.issn Print: 2521-0262
dc.identifier.issn Online: 2662-012X
dc.description Journal article published in African Perspectives of Research in Teaching & Learning (APORTAL) Vol 7 (1) (2023) en_US
dc.description.abstract This cross-sectional quantitative study sought to identify factors associated with the performance of first-year pharmacy students. It made use of secondary data obtained from the Department of Pharmaceutical Sciences at Tshwane University of Technology (TUT). Even after adjusting for Grade 12 science subjects, the results of hierarchical logistic regression models show that male students were slightly less likely than female students to pass the first year of pharmacy in 2015, 2016, and 2017. Academic performance predictors could be used to reconfigure admissions criteria. As a result, a better understanding of the factors influencing pharmacy student performance may aid pharmacy educators in developing effective interventions to improve student performance. Identifying new predictors of academic performance may assist the TUT pharmacy school to retain and graduate better pharmacists. This study suggests that a similar study should be conducted using structural equation models and hierarchical regression models to confirm the current results using a data set containing other important predictors mentioned in previous studies. en_US
dc.format.extent 19 pages en_US
dc.language.iso en en_US
dc.publisher African Perspectives of Research in Teaching & Learning (APORTAL) en_US
dc.relation.requires PDF en_US
dc.subject Hierarchical logistic regression model en_US
dc.subject Pharmacy programme en_US
dc.subject Student performance en_US
dc.subject Selection criteria en_US
dc.subject Predictors en_US
dc.subject.lcsh Pharmacy students en_US
dc.subject.lcsh Academic achievement en_US
dc.subject.lcsh College students en_US
dc.title Predictors of pharmacy students' performances in first year at a University of Technology in Gauteng Province : analysis using hierarchical regression models en_US
dc.type Article en_US

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