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Substance use disorder (SUD) is one of the most pressing public heath crises in the United States, yet it remains one of the most highly stigmatized health conditions. Not only is addiction stigmatized, but so too are harm reduction practices and treatment efforts, such as safe injection sites and medications for opioid use disorder (MOUD). Extant scholarship has documented the deleterious consequences of SUD stigma with respect to healthcare, employment, housing, and relationships. However, less is known about the ways that public stigma shapes views about harm reduction and treatment, and the extent to which different dimensions of stigma matter most for different harm reduction approaches. The present study, therefore, uses nationally representative data (2024 Shatterproof Addiction Stigma Index, N = 8,192) to examine the association between three dimensions of SUD stigma – public stigma (i.e., social distance and prejudice), structural stigma (i.e., policy opinions), and stigma against medications for opioid use disorder (MOUD stigma) – and willingness to support four harm reduction practices – distribution of Naloxone, access to free fentanyl testing strips, establishment of safe injection sites, and support for a neighborhood MOUD clinic. Results indicate that all three stigma dimensions independently predict opposition to harm reduction measures, controlling for causal attribution, familiarity with SUDs, and SES. Public stigma and structural stigma differentially predict opposition to harm reduction, such that greater public stigma is more strongly associated with opposition to measures that are more socially and physically proximate, while structural stigma activates opposition towards resource-based outcomes. Unsurprisingly, MOUD stigma is the strongest predictor of opposition to a neighborhood MOUD clinic. Overall, lower support for harm reduction is likely driven by distinct processes across outcomes, meaning that messaging and policy advocacy should target specific stigmas for specific harm reduction support models.