Feeling Blue: the Impact of Body-Worn Cameras on Police Officers' Well-Being During Investigations of Misconduct
Bibliographic record
Abstract
Police misconduct investigations are critical to maintaining public trust and keeping law enforcement agencies accountable. Still, they can be prolonged, stressful processes for the subject officers (and complainants) involved. Through three interrelated studies, the current dissertation makes two important contributions to the research literature-it highlights police misconduct as a salient stressor in policing and it draws attention to the potential use of body-worn cameras (BWCS) as a mental wellness tool. Study 1 employs a vignette-based experimental design to examine how misconduct allegation severity, allegation truthfulness, and BWC presence influence expected officer stress in the ensuing misconduct investigation, confidence in the available evidence, expected investigation duration, and the likelihood of a guilty verdict being reached. Findings suggest that officers expect lower stress levels, particularly in cases where the misconduct allegations are false, higher confidence in the evidence, and shorter investigations when BWC footage is available. Study 2 utilizes semi-structured interviews with officers who have been involved in misconduct investigations. This study provides qualitative insights into the lived experiences of officers, highlighting the chronic stress, uncertainty, and moral injury they endure as a result of an allegation or investigation of misconduct. Officers described investigative delays and a lack of procedural transparency as stressors within the complaints process and viewed BWCs as a potential tool for reducing ambiguity, expediting case resolution, and enhancing officer wellness. Study 3 shifts focus to public perceptions of police misconduct investigations, oversight mechanisms, and the role of BWCs in investigations. Using a nationwide survey, this study finds a public preference for civilian oversight models. However, this preference lessened when participants scored higher on perceptions of police legitimacy. The public also recognizes the stress experienced by both officers and complainants involved in misconduct investigations and largely supports BWCs as a mechanism for transparency and efficiency.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".