A Cross-Sectional Study of the Relationship between Previous Military Experience and Mental Health Disorders in Currently Serving Public Safety Personnel in Canada
Bibliographic record
Abstract
OBJECTIVE: There is an increased incidence of some mental health disorders such as post-traumatic stress disorder (PTSD) in some members of the military and in some public safety personnel (PSP) such as firefighters, police officers, paramedics, and dispatchers. Upon retirement from the armed forces, many individuals go on to second careers as PSP. Individuals with prior military experience may be at even greater risk than nonveterans for developing mental health disorders. The present study was designed to examine the relationship between prior military service and symptoms of mental health disorders in PSP. METHODS: This is a cross-sectional, observational study. Data for this study were collected from an anonymous, web-based, self-report survey of PSP in Canada. Invitations to participate were sent to PSP via their professional organizations. Indications of mental disorder(s) and symptom severity were assessed using well-validated self-report screening measures. RESULTS: Of the survey respondents who provided this information, 631 (6.8%) had prior armed forces experience; however, not all responses were complete. Ex-military PSP reported significantly more exposure to traumatic events and were approximately 1.5 times more likely to screen positive for indications of PTSD, mood, anxiety, or acute stress disorders and to have contemplated suicide than those without prior armed forces experience. CONCLUSIONS: In our study, individuals in PSP with prior service experience in the armed forces were more likely to screen positive for indicators of some mental health disorders. Accordingly, mental health practitioners should inquire about previous service in the armed forces when screening, assessing, and treating PSP.
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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 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".