Differences in Mental Health, Help-Seeking and Barriers to Care Between Civilians and Sworn Members Working in Law Enforcement: A Research Note
Bibliographic record
Abstract
Abstract Recent Canadian research indicates 44.5% of public safety personnel (PSP) self-report symptoms consistent with at least one type of mental disorder; however, researchers have typically not focused on the mental wellness of civilians working within PSP sectors. Given that the number of civilians working in Canadian law enforcement organizations has doubled since 2003, with more than 30% of all police personnel in Canada being civilians, more research is needed to support this understudied sub-population within law enforcement. The current study used a survey to compare civilian members (n = 80) and sworn (n = 112) police officers working within a law enforcement organization on issues regarding mental disorders, perceived barriers to care and help-seeking behaviours. Results indicate that civilian members self-report a high prevalence of mental disorders and lower resilience compared with police officers in the same organization. Civilians reported similar barriers to accessing mental health compared with police officers but were less likely to indicate willingness to access supports within their place of employment. Our results support the need for equitable access to mental health resources for civilian staff working within law enforcement organizations.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".