Mitigating the prevalence of PTSD amongst police officers: the perspective of supervisors’ in the Royal Canadian Mounted Police
Bibliographic record
Abstract
Purpose Police officers are at particular risk of developing Post-Traumatic Stress Disorder (PTSD) which can impact their work and life (Foley and Massey, 2021). However, workplace support can mitigate this risk. The purpose of this research study was to understand, from a police officer’s perspective, the mental health needs of members and the best opportunities to provide support for officers, which can mitigate the prevalence of PTSD. Design/methodology/approach The current study included semi-structured interviews with eight police officers who hold supervisory positions as non-commissioned officers, either corporals or sergeants, in the Royal Canadian Mounted Police (RCMP). A Thematic Analysis yielded three overarching themes: Standing in Between – The Nature of the Supervisor Role, The Available vs the Accessible and In between Acceptance and Scepticism. Findings Overall, the themes depicted both effective and ineffective measures in the force’s current provision for mental health support and organizational barriers to accessing existing support. It also uncovered the embedded tension within the supervisory role and areas for improvement. Conclusions highlight the need to review some existing measures and policies to improve the accessibility and viability of available support as well as facilitate change in culture and members’ attitudes towards help-seeking. Originality/value This paper provides insight into a niche demographic of individuals, police officers with PTSD and provides a perspective of Canadian RCMP officers, of which there is very limited research on.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.008 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".