Police Body-Worn Cameras: Development of the Perceived Intensity of Monitoring Scale

Abstract

Body-worn cameras (BWCs) are the latest and perhaps most tangible answer to complex social questions regarding the use of force, state legitimacy, and the proper role of police in a liberal democracy. How do officers experience heightened monitoring? This article pursues two objectives via two studies. In the first study, we establish a valid and reliable scale to measure police officer perceptions of the risks posed to them by the recording and distribution of BWC footage, conceptualized as Perceived Intensity of Monitoring (PIM). Based on a survey of 617 police officers, we evaluate an 11-item questionnaire and assess internal consistency and construct validity, perform exploratory factor analysis, and derive a PIM Scale composed of three factors measuring officer perceptions of discretion, disapproval, and distribution effects. In the second study, we evaluate the PIM Scale’s ability to predict officer emotional exhaustion, discriminating between BWC and non-BWC equipped officers. This study contributes to evolving work in BWC research by developing a useful measure for police administrators and practitioners charged with making decisions related to BWC implementation and policy. Further, the PIM Scale is applicable across professions other than policing, as surveillant workplace monitoring and technologies of accountability continue to expand to other contexts.

Publication
Criminal Justice Review

Summary

Researchers developed a new measurement tool to understand how police officers feel about being monitored by body-worn cameras, finding that officers worry about three main things: losing their ability to make judgment calls, facing public criticism, and having their footage shared widely. Officers who feel more intensely monitored by these cameras experience higher levels of emotional exhaustion and job stress. This research helps police departments better understand how body cameras affect officer wellbeing and job performance, which is crucial for making smart decisions about camera policies.

(AI-generated summary, v1, January 2026)

Citation Information

Citations: 29 (as of January 2026)

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