The Wellbeing Tri-Hazard: an evidence-informed model for medical student wellbeing

<ns5:p>Wellbeing is a central concern in medical education, yet the term remains inconsistently defined and difficult for educators to measure, let alone meaningfully remediate. Although many wellbeing initiatives focus on specific interventions—such as resiliency training, mindfulness, or support services—these approaches often fail to explain how wellbeing becomes destabilized during the realities of medical training. This Opinion Article introduces the Wellbeing Tri-Hazard model as an evidence-informed conceptual framework for scholarly discussion. The model is grounded in previously published literature on wellbeing, resiliency, medical education, student support, and systems-level influences. I define wellbeing as an ecosystem underpinned by cognitive, physical, and institutional components. Wellbeing is achieved when this tri-dimensional relationship is in balance, creating a positive impact on mental and physical health, which promotes motivation, self-regulation and resiliency. The Wellbeing Tri-Hazard model shows how disruption in one domain can cascade across physical, cognitive, and institutional dimensions—potentially weakening resiliency and reducing autonomy, engagement, and sense of value. The key physical-domain components identified in the literature are exercise, nutrition, mindfulness, sleep hygiene, chronic health conditions, socioeconomic factors, substance abuse, and work–life balance. The model can help educators discuss wellbeing across student-level behavior, curriculum and faculty support, and stakeholder or policy responsibility. Following the model’s conceptualization, subseque,rnt practice-informed reflection has strengthened my view that the model may have relevance beyond undergraduate medical education. However, this reflection is not presented as new data. The model has not yet been empirically validated and should not be treated as a completed framework. Instead, I offer the Wellbeing Tri-Hazard as a citable model for open critique, refinement, and future empirical testing.</ns5:p>