Police officers’ perceptions and adaptation to body-worn cameras during mental health calls
Bibliographic record
Abstract
Purpose Police officers increasingly deal with individuals with mental health problems. These interactions are more likely to result in the use of force and fatalities. To monitor these situations, several experts have recommended the use of body-worn cameras (BWCs) by police organizations. Past research and evaluations have overlooked how BWCs may influence mental health-related interventions, creating a knowledge gap regarding how BWC policies should oversee them. This paper, thus, aims to draw upon the street-level bureaucracy framework to explore how police officers perceive the usefulness of BWCs during interventions involving mental health issues and how they exercise discretion in activating them. Design/methodology/approach The authors analyzed comments on mental health-related interventions captured by BWCs from 83 interviews with 61 police officers. Findings The findings shed light on how BWCs may positively or negatively affect interactions with individuals with mental health issues and how officers adapt their activation practices to mitigate potential negative impacts and enhance potential positive effects on their work. Practical implications This study reiterates that the use of BWCs is not a simple solution for mental health-related calls, highlighting the need for a formal evaluation after implementation. It calls for BWC activation policies that reflect the dynamic and complex realities of police work. It does not advocate for a total ban on recording, mandatory filming or unrestricted officer discretion, but rather a balanced approach. It calls for policies that are both aligned with police leaders objectives and street-level officers’ ability to develop alternative and adaptative practices. Originality/value This study provides guidance for policymakers in developing BWC policies that will improve police–civilian interactions in the context of mental health crises while considering the ability of street-level officers to create their own alternative practices.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".