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Predicting Nurses’ Views on Decriminalization of Psychedelics

Carolyn M. Porta, Cheng-Chang Wu, Anna Graefe, Nathan Grumdahl, Caroline Dorsen

Psychedelic Medicine February 8, 2026 DOI: 10.1177/28314425251385531

Summary

AI-generated from the abstract

A survey of 1,092 registered nurses in Minnesota found that age, gender identity, spiritual orientation, awareness of Colorado's psychedelic decriminalization, and attitudes about psychedelics' legal status and effects best predict whether a nurse supports decriminalization. The predictive model showed excellent accuracy, with a multiclass AUC of 0.870. Younger age, awareness of Colorado's laws, and less concern about psychedelics' effects and legal status were associated with more positive attitudes. The model provides a starting point for tailoring educational resources on psychedelics for nurses nationwide.

Study at a glance

Characteristics Secondary analysis of e-survey data Peer reviewed
Sample size 1,092
Population Registered nurses and advanced practice registered nurses in Minnesota
Key finding Age, gender identity, spiritual orientation, awareness of Colorado's psychedelic decriminalization, and attitudes toward psychedelics' legal status and effects predict nurses' support for decriminalization.

Abstract

Background: Despite being the largest sector of the health care workforce with potential to support advancing psychedelics in health care, nurses and their views toward psychedelics and changing legal restrictions remain mostly unknown. Aims: To identify an optimal predictive model for nurses’ support (or not) of decriminalization of psychedelics. Methods: Secondary analysis of e-survey data from 1092 registered nurses and advanced practice registered nurses invited randomly from the Minnesota Board of Nursing registry. To develop an optimal predictive model, multinomial logistic regression with backward selection using the Akaike information criterion (AIC) was applied to the training set. The final model was fitted to the validation set for effect estimation, with performance assessed using multiclass area under the curve (AUC). Results: Backward selection using AIC identified several key predictors of support for decriminalization of psychedelics in Minnesota: age, gender identity, specific spiritual orientation, awareness of Colorado’s psychedelic decriminalization, and scores from the Attitudes and Perceptions Questionnaire legal and effects subscales. The final model demonstrated excellent discriminative ability with a multiclass AUC of 0.870 (95% confidence interval [CI]: 0.847–0.898). Pairwise comparisons revealed outstanding discrimination between supporters and opponents of decriminalization (AUC = 0.973, 95% CI: 0.952–0.987). Conclusion: Age (younger/older), (awareness/lack of awareness) of Colorado’s decriminalization laws, and attitudes (concerns/lack of concerns) toward effects and legal status of psychedelics predicted a nurse’s (more/less positive) attitude toward psychedelics. This model offers an informative starting point for understanding nurses’ and, by proxy, the communities in which they live and work and provides initial direction toward tailoring curricular and professional development resources on psychedelics for nurses across the United States.

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