Associations between admissions factors and the need for remediation

Advances in Health Sciences Education(2022)

引用 1|浏览1
暂无评分
摘要
This study examines the way in which student characteristics and pre-admissions measures are statistically associated with the likelihood a student will require remediation for academic and professionalism offenses. We anchor our inquiry within Irby and Hamstra’s (2016) conceptual framework of constructs of professionalism. Data from five graduating cohorts (2014–2018) from McMaster University (Hamilton, Canada) (N = 1,021) were retroactively collected and analyzed using traditional and multinominal logistic regression analyses. The relationship among student characteristics, pre-admissions variables, and referral for potential remediation both by occurrence (yes/no) as well as type (academic/professional/no referral) were examined separately. Findings indicate that gender (OR = 0.519, 95% CI 0.326–0.827, p < 0.01) and undergraduate grade point average (GPA) (OR = 0.245, 95% CI 0.070–0.855, p < 0.05) were significantly associated with instances of referral for potential professionalism and academic remediation, respectively. Women were less likely than men to require remediation for professionalism (OR = 0.332, 95% CI 0.174–0.602, p < 0.001). Undergraduate GPAs (OR = 0.826, 95% CI 0.021–0.539, p < 0.01) were significantly associated with remediation for academic reasons. Lower undergraduate GPAs were associated with a higher likelihood of remediation. These findings point to the admissions variables that are associated with instances that prompt referral for potential remediation. Where associations are not significant, we consider the application of different conceptualizations of professionalism across periods of admissions and training. We encourage those involved in applicant selection and student remediation to emphasize the importance of the interactions that occur between personal and contextual factors to influence learner behaviour and professional identity formation.
更多
查看译文
关键词
Admissions and selection,Remediation,Quantitative methods
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要