Assessing the Impact of Clinical Supervision Models on Professional Growth of Graduate Nurses

Posted: Jun 12, 2023

• Brandon Mills
• Theo Simmons
• Ivy Coleman

Abstract

This research presents a novel computational framework for evaluating clinical supervision models in nursing education through the application of network analysis and machine learning techniques. Traditional assessment methods in nursing education have primarily relied on qualitative approaches and self-reported measures, which often lack the scalability and objectivity required for comprehensive evaluation. Our study introduces an innovative methodology that transforms clinical supervision interactions into quantifiable network structures, enabling the identification of previously unrecognized patterns in professional development trajectories. We developed a multi-dimensional assessment protocol that captures both explicit competencies and implicit professional growth indicators across three distinct supervision models: traditional hierarchical supervision, peer-collaborative supervision, and hybrid adaptive supervision. The research employed natural language processing to analyze supervision session transcripts, social network analysis to map professional relationship dynamics, and reinforcement learning algorithms to model professional growth pathways. Our findings reveal that hybrid adaptive supervision models generate significantly more diverse professional networks and foster accelerated competency development compared to traditional approaches. The computational framework demonstrated 89