# Implementing Peer Learning in Clinical Nursing Education: Addressing Challenges in High-Pressure Healthcare Systems—An Interview Study

## TL;DR

This study explores how peer learning works in nursing education, focusing on challenges like nurse shortages and the need for flexible models.

## Contribution

The study provides insights into implementing peer learning in high-pressure healthcare systems with nurse shortages.

## Key findings

- Peer learning improved supervision quality and recruitment opportunities.
- Challenges included varying student competence and lack of registered nurse supervisors.
- Flexible models are needed to address nurse shortages.

## Abstract

Peer learning is an educational strategy used in clinical training for nursing students that has proven to be beneficial for both students and supervisors.

This study aimed to describe the perceptions and experiences of first-line managers, clinical supervisors, and educators in nursing with peer learning (PL) as a supervision model. It also aimed to describe the implementation process, focusing on key factors and obstacles to successful implementation in healthcare systems facing pressure from registered nurse (RN) shortages.

This qualitative descriptive interview study involved individual and focus group interviews with 20 participants from three groups: first-line managers, clinical supervisors, and educators in nursing. Interviews were analyzed using deductive content analysis, guided by the i-PARIHS framework.

While PL improved supervision quality, adaptability, and recruitment opportunities, it required active engagement from staff and students, and ongoing collaboration with the university. Experienced challenges were students with varying competence levels and inadequate continuity due to the shortage of RNs. Participants found the introductory activities and scheduled meetings valuable but faced challenges related to time allocation and a lack of RNs to act as clinical supervisors.

Despite PL's benefits, the RN shortage hinders its implementation, necessitating the development of flexible models that can be implemented despite the shortage.