Abstract
Background: Education literature worldwide is replete with studies evaluating the effectiveness of Multiple Mini Interviews (MMIs) in admissions to medicine but <1% of published studies have been conducted in selection to nursing and midwifery programmes. Objectives: To examine the predictive validity of MMIs using end of programme clinical and academic performance indicators of pre-registration adult, child, and mental health nursing and midwifery students. Design and setting: A cross-sectional cohort study at one university in the United Kingdom. Participants: A non-probability consecutive sampling strategy whereby all applicants to the September 2015 pre-registration adult, child, mental health nursing and midwifery programmes were invited to participate. Of the 354 students who commenced year one, 225 (64%) completed their three-year programme and agreed to take part (adult 120, child 32, mental health nursing 30 and midwifery 43). Methods: All applicants were interviewed using MMIs with six and seven station, four-minute models deployed in nursing and midwifery student selection respectively. Associations between MMI scores and the cross-discipline programme performance indicators available for each student at this university at the end of year three: clinical practice (assessed by mentors) and academic attainment (dissertation mark) were explored using multiple linear regression adjusting for applicant age, academic entry level, discipline and number of MMI stations. Results: In the adjusted models, students with higher admissions MMI score (at six and seven stations) performed better in clinical practice (p < 0.001) but not in academic attainment (p = 0.122) at the end of their three-year programme. Conclusion: These findings provide the first report of the predictive validity of MMIs for performance in clinical practice using six and seven station models in nursing and midwifery programmes. Further evidence is required from both clinical and academic perspectives from larger, multi-site evaluations.