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Police officer attitudes toward pre-arrest behavioral health diversion programs: identifying determinants of support for deflection using a machine learning method

Ellen A. Donnelly (Center for Drug and Health Studies, University of Delaware, Newark, Delaware, USA) (Department of Sociology and Criminal Justice, University of Delaware, Newark, Delaware, USA)
Madeline Stenger (Department of Psychiatry and Behaviorial Sciences, Duke University School of Medicine, Durham, North Carolina, USA) (Wilson Center for Science and Justice, Duke University Law School, Durham, North Carolina, USA)
Daniel J. O'Connell (Center for Drug and Health Studies, University of Delaware, Newark, Delaware, USA) (Department of Sociology and Criminal Justice, University of Delaware, Newark, Delaware, USA)
Adam Gavnik (Center for Drug and Health Studies, University of Delaware, Newark, Delaware, USA) (Department of Sociology and Criminal Justice, University of Delaware, Newark, Delaware, USA)
Jullianne Regalado (Center for Drug and Health Studies, University of Delaware, Newark, Delaware, USA) (Department of Sociology and Criminal Justice, University of Delaware, Newark, Delaware, USA)
Laura Bayona-Roman (Center for Drug and Health Studies, University of Delaware, Newark, Delaware, USA) (Department of Sociology and Criminal Justice, University of Delaware, Newark, Delaware, USA)

Policing: An International Journal

ISSN: 1363-951X

Article publication date: 19 April 2024

11

Abstract

Purpose

This study explores the determinants of police officer support for pre-arrest/booking deflection programs that divert people presenting with substance use and/or mental health disorder symptoms out of the criminal justice system and connect them to supportive services.

Design/methodology/approach

This study analyzes responses from 254 surveys fielded to police officers in Delaware. Questionnaires asked about views on leadership, approaches toward crime, training, occupational experience and officer’s personal characteristics. The study applies a new machine learning method called kernel-based regularized least squares (KRLS) for non-linearities and interactions among independent variables. Estimates from a KRLS model are compared with those from an ordinary least square regression (OLS) model.

Findings

Support for diversion is positively associated with leadership endorsing diversion and thinking of new ways to solve problems. Tough-on-crime attitudes diminish programmatic support. Tenure becomes less predictive of police attitudes in the KRLS model, suggesting interactions with other factors. The KRLS model explains a larger proportion of the variance in officer attitudes than the traditional OLS model.

Originality/value

The study demonstrates the usefulness of the KRLS method for practitioners and scholars seeking to illuminate patterns in police attitudes. It further underscores the importance of agency leadership in legitimizing deflection as a pathway to addressing behavioral health challenges in communities.

Keywords

Acknowledgements

This work was supported by the Delaware Statewide Comprehensive Opioid Abuse Program (COAP) Saving Lives Initiative, funded by the Bureau of Justice Assistance (2019-AR-BX-K047) through the Delaware Criminal Justice Council (CJC). Views expressed in this article are those of the authors. We thank the CJC for its coordination efforts and Delaware’s sworn officers who participated in the survey.

Citation

Donnelly, E.A., Stenger, M., O'Connell, D.J., Gavnik, A., Regalado, J. and Bayona-Roman, L. (2024), "Police officer attitudes toward pre-arrest behavioral health diversion programs: identifying determinants of support for deflection using a machine learning method", Policing: An International Journal, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/PIJPSM-11-2023-0158

Publisher

:

Emerald Publishing Limited

Copyright © 2024, Emerald Publishing Limited

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