Facilitating learning on clinical placement using near-peer supervision: A mixed methods study

Abstract

BACKGROUND: Graduating nursing students report lower competence in leadership and delegation skills, which may be due to lack of sufficient opportunities to practice leadership skills such as delegation and supervision. A near-peer clinical supervision model, in which third-year students supervise first-year students on placement, may provide a mechanism to develop graduating students' leadership skills while improving the learning experience for junior students. OBJECTIVES: To evaluate nursing students' experiences and perceptions of participating in a near-peer clinical supervision model. DESIGN: A mixed methods design including an anonymous post-placement survey of students, and a group interview. SETTINGS: Medical and surgical wards in three Australian hospitals. METHOD: Forty-three first-year nursing students were supervised by 92 third-year nursing students on clinical placement under the supervision of a registered nurse in a near-peer supervision model. RESULTS: Twenty-seven first-year (69.2%) and 43 third-year (46.7%) students completed the questionnaire. First-years reported that being supervised by a senior student was a positive experience and would recommend it to other students (4.49/5 ± 0.71), and indicated that third-year students behaved professionally, were knowledgeable, and provided opportunities to ask questions (4.52-4.81/5). Third-year students reported gaining confidence, teaching, delegation and leadership skills (4.21-4.49/5). Qualitative responses supported the quantitative findings. Additional findings were the need for greater preparation of ward registered nurses to work in the model. CONCLUSIONS: Both groups enjoyed working in a near-peer clinical supervision model. The model provided opportunities for senior students to develop leadership and delegation skills and a positive experience of placement for junior students. Further attention to preparation of ward registered nurses would improve model delivery.