Alison Callwood1, Debbie Cooke2, Sarah Bolger3, Agnieszka Lemanska4, Helen Allan5. 1. School of Health Sciences, University of Surrey, Guildford, Surrey, GU2 7XH, UK. Electronic address: a.callwood@surrey.ac.uk. 2. School of Health Sciences, University of Surrey, Guildford, Surrey, GU2 7XH, UK. Electronic address: d.cooke@surrey.ac.uk. 3. School of Health Sciences, University of Surrey, Guildford, Surrey, GU2 7XH, UK. Electronic address: s.bolger@surrey.ac.uk. 4. School of Health Sciences, University of Surrey, Guildford, Surrey, GU2 7XH, UK. Electronic address: a.lemanska@surrey.ac.uk. 5. Centre for Critical Research in Nursing and Midwifery, School of Health and Education, Middlesex University, UK. Electronic address: h.allan@mdx.ac.uk.
Abstract
BACKGROUND: Universities in the United Kingdom (UK) are required to incorporate values based recruitment (VBR) into their healthcare student selection processes. This reflects an international drive to strengthen the quality of healthcare service provision. This paper presents novel findings in relation to the reliability and predictive validity of multiple mini interviews (MMIs); one approach to VBR widely being employed by universities. OBJECTIVES: To examine the reliability (internal consistency) and predictive validity of MMIs using end of Year One practice outcomes of under-graduate pre-registration adult, child, mental health nursing, midwifery and paramedic practice students. DESIGN: Cross-discipline evaluation study. SETTING: One university in the United Kingdom. PARTICIPANTS: Data were collected in two streams: applicants to A) The September 2014 and 2015 Midwifery Studies programmes; B) September 2015 adult; Child and Mental Health Nursing and Paramedic Practice programmes. Fifty-seven midwifery students commenced their programme in 2014 and 69 in 2015; 47 and 54 agreed to participate and completed Year One respectively. 333 healthcare students commenced their programmes in September 2015. Of these, 281 agreed to participate and completed their first year (180 adult, 33 child and 34 mental health nursing and 34 paramedic practice students). METHODS: Stream A featured a seven station four-minute model with one interviewer at each station and in Stream B a six station model was employed. Cronbach's alpha was used to assess MMI station internal consistency and Pearson's moment correlation co-efficient to explore associations between participants' admission MMI score and end of Year one clinical practice outcomes (OSCE and mentor grading). RESULTS: Stream A: Significant correlations are reported between midwifery applicant's MMI scores and end of Year One practice outcomes. A multivariate linear regression model demonstrated that MMI score significantly predicted end of Year One practice outcomes controlling for age and academic entry level: coefficients 0.195 (p=0.002) and 0.116 (p=0.002) for OSCE and mentor grading respectively. In Stream B no significant correlations were found between MMI score and practice outcomes measured by mentor grading. Internal consistency for each MMI station was 'excellent' with values ranging from 0.966-0.974 across Streams A and B. CONCLUSION: This novel, cross-discipline study shows that MMIs are reliable VBR tools which have predictive validity when a seven station model is used. These data are important given the current international use of different MMI models in healthcare student selection processes.
BACKGROUND: Universities in the United Kingdom (UK) are required to incorporate values based recruitment (VBR) into their healthcare student selection processes. This reflects an international drive to strengthen the quality of healthcare service provision. This paper presents novel findings in relation to the reliability and predictive validity of multiple mini interviews (MMIs); one approach to VBR widely being employed by universities. OBJECTIVES: To examine the reliability (internal consistency) and predictive validity of MMIs using end of Year One practice outcomes of under-graduate pre-registration adult, child, mental health nursing, midwifery and paramedic practice students. DESIGN: Cross-discipline evaluation study. SETTING: One university in the United Kingdom. PARTICIPANTS: Data were collected in two streams: applicants to A) The September 2014 and 2015 Midwifery Studies programmes; B) September 2015 adult; Child and Mental Health Nursing and Paramedic Practice programmes. Fifty-seven midwifery students commenced their programme in 2014 and 69 in 2015; 47 and 54 agreed to participate and completed Year One respectively. 333 healthcare students commenced their programmes in September 2015. Of these, 281 agreed to participate and completed their first year (180 adult, 33 child and 34 mental health nursing and 34 paramedic practice students). METHODS: Stream A featured a seven station four-minute model with one interviewer at each station and in Stream B a six station model was employed. Cronbach's alpha was used to assess MMI station internal consistency and Pearson's moment correlation co-efficient to explore associations between participants' admission MMI score and end of Year one clinical practice outcomes (OSCE and mentor grading). RESULTS: Stream A: Significant correlations are reported between midwifery applicant's MMI scores and end of Year One practice outcomes. A multivariate linear regression model demonstrated that MMI score significantly predicted end of Year One practice outcomes controlling for age and academic entry level: coefficients 0.195 (p=0.002) and 0.116 (p=0.002) for OSCE and mentor grading respectively. In Stream B no significant correlations were found between MMI score and practice outcomes measured by mentor grading. Internal consistency for each MMI station was 'excellent' with values ranging from 0.966-0.974 across Streams A and B. CONCLUSION: This novel, cross-discipline study shows that MMIs are reliable VBR tools which have predictive validity when a seven station model is used. These data are important given the current international use of different MMI models in healthcare student selection processes.