Demographics of experts in each Delphi round.

Introduction
There is a worldwide dearth in literature on the nature, causes, and consequences of structural stigma in mental healthcare. This study aimed to address this gap by exploring key components for measuring structural stigma in healthcare system settings.
Methods
We used a modified Delphi method consisting of 3 rounds with global experts (stigma researchers, persons with lived experiences of mental health conditions (PWLEs), and policymakers). In the first round, indicators identified through a literature review (n = 39 studies) were appraised through expert consultation workshops with 22 panellists, including 54.5% women, 41% PWLEs, and 68.2% from low-and-middle income countries (LMICs). Round 2 (n = 53 panellists; 51% women, 8.3% PWLEs, and 56.6% from LMICs) involved ranking indicators through an online survey, and Round 3 (n = 58 panellists; 46% women, 21.7% PWLEs, and 60.4% from LMICs) involved re-ranking the results from Round 2. Smith’s salience index was calculated to measure consensus and Kendall’s coefficient of concordance to determine the degree of agreement. Narrative opinions and feedback from panellists during all three Delphi rounds were also sought.
Results
A list of indicators within five core measurement domains was identified in Round 1. Round 2 results were heterogeneous as indicated by the low to moderate salience of most indicators. Round 3 resulted in 4–5 indicators in each domain, that were ranked as highly salient by the expert panellists. Experts also provided narrative feedback on the definition of structural stigma, barriers to its measurement, domain-specific comments, and indicators-specific comments.
Conclusion
The framework aids in defining mental health-related structural stigma in healthcare and framing it in terms of inequities within healthcare system structures. These structures result in negative experiences of PWLEs and limit their access to quality healthcare. This conceptualization, informed by PWLE and stakeholders in LMICs, makes it easier to measure structural stigma and monitor changes in diverse healthcare settings around the world.