Despite decades of policy efforts, medical school populations in the UK have remained largely unchanged, with underrepresentation of students from disadvantaged backgrounds continuing to be a major concern.


As documented globally, traditional selection methods - focused heavily on academic metrics - fail to account for contextual disadvantage, thereby perpetuating inequity. One of the reasons is that schools have become stratified; the best grades are concentrated in the best schools, and there is unequal access to those schools. This presentation introduces a novel approach to contextualizing applicant data, which adjusts selection criteria based on individual circumstances, in this case, using applicant attainment relative to their school average attainment. This approach can be applied to every applicant, and does not treat one group of applicants more favourably than another. It only changes the metric used to judge them.


This approach demonstrates that contextual admissions can significantly improve diversity without compromising academic standards, emphasizing that equity in selection does not mean lowering expectations but rather recognizing and adjusting for unequal starting points. Further, this work calls for a shift from binary thinking (e.g., merit vs. fairness) to more nuanced, systemic approaches that embrace complexity and uncertainty. To ensure accuracy and equity, healthcare systems across the globe must consider long-term, intentional strategies that integrate data, policy, and stakeholder perspectives.


In sum, the presentation offers a compelling case for rethinking selection into healthcare careers through a lens of contextual equity, highlighting the need for innovative, evidence-informed, and socially responsive selection practices. As the legal framework around admissions and selection changes, a universal procedure that recognizes "distance travelled" and does not compromise the academic quality of selection may be useful.