Mental Health Disorder Symptoms among Canadian Coast Guard and Conservation and Protection Officers
Bibliographic record
Abstract
Canadian public safety personnel (PSP) screen positive for one or more mental health disorders, based on self-reported symptoms, at a prevalence much greater (i.e., 44.5%) than the diagnostic prevalence for the general public (10.1%). Potentially psychologically traumatic event (PPTE) exposures and occupational stressors increase the risks of developing symptoms of mental health disorders. The current study was designed to estimate the mental health disorder symptoms among Canadian Coast Guard (CCG) and Conservation and Protection (C&P) Officers. The participants (n = 412; 56.1% male, 37.4% female) completed an online survey assessing their current mental health disorder symptoms using screening measures and sociodemographic information. The participants screened positive for one or more current mental health disorders (42.0%; e.g., post-traumatic stress disorder, major depressive disorder, generalized anxiety disorder, social anxiety disorder, panic disorder, alcohol use disorder) more frequently than in the general population diagnostic prevalence (10.1%; p < 0.001). The current results provide the first information describing the prevalence of current mental health disorder symptoms and subsequent positive screenings of CCG and C&P Officers. The results evidence a higher prevalence of positive screenings for mental health disorders than in the general population, and differences among the disorder-screening prevalence relative to other Canadian PSP. The current results provide insightful information into the mental health challenges facing CCG and C&P PSP and inform efforts to mitigate and manage PTSI among PSP. Ongoing efforts are needed to protect CCG and C&P Officers’ mental health by mitigating the impacts of risk factors and operational and organizational stressors through interventions and training, thus reducing the prevalence of occupational stress injuries.
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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 | 0.002 |
| 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".