High Rates of Mental Health Disorders in Civilian Employees Working in Police Organizations
Bibliographic record
Abstract
Working in a police organization often involves being exposed to potentially traumatic events and stressful circumstances regardless of occupation or rank. Police mental health is a public health concern, but the mental health of civilian employees working in police organizations has been much less studied. The current study aims to compare the frequency of mental health conditions in both police officers and civilians. This was evaluated by measuring mean scores on several mental health screening tools including scales to determine symptom severity for posttraumatic stress disorder (PTSD) with the PTSD Checklist - PCL-5, depression with the Patient Health Questionnaire-9 (PHQ), general anxiety with the Generalized Anxiety Disorder 7-item scale (GAD-7), and alcohol use with the Alcohol Use Disorders Identification Test (AUDIT). The total potential population was 1,225 civilian employees and 3,714 police officers, of which 513 (10%) participated. Of these, 201 (16%) were civilians, and 312 (8%) were police officers (p<0.001). In the study population, 26% screened positive for any mental health disorder. Somewhat surprisingly, we found significantly more civilians (32.8%) than police officers (22.7%) met diagnostic criteria. We also found that civilian participants had higher mean scores in measures of PTSD, anxiety, and depression, although only for depression did this reach statistical significance. Civilians were 1.7 times more likely to screen positive for depression compared to police officers, a statistically significant difference. In contrast, police officers demonstrated statistically higher scores for alcohol use than civilians. One limitation of this study is that the data reflects responses from only a minority of the overall population and, therefore, may not accurately reflect the frequency of mental health issues in the total police organization including civilian employees. Nonetheless, the results strongly suggest that the mental health of all employees can be negatively impacted by working in a police environment, and this is important given the growing number of civilians employed within police organizations. These findings support initiatives aimed at destigmatizing mental health disorders, improving stress management, and increasing access to mental health care on an organization-wide basis, and not just limited to front-line police officers.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".