Mental disorder symptoms in Canadian HEMS personnel: a national-level study
Bibliographic record
Abstract
Emergency services personnel are regularly exposed to potentially traumatic events with substantial consequences for their mental health. Current estimations from Canadian emergency medical service (EMS) providers show a prevalence of clinically relevant symptomology of 20% or higher in anxiety, depression, and post-traumatic stress. Interestingly, evidence from Canadian helicopter emergency medical services (HEMS) demonstrates a substantially lower prevalence rate (i.e., <10%) of mental disorder symptoms. However, current Canadian data stem from a single HEMS service. A comprehensive assessment of mental disorders from a larger, nationwide sample is presently absent, which was the purpose of the current study. A sample of 215 HEMS personnel (male n = 165, 76.6%) from six Canadian provinces (AB, BC, SK, MB, ON, NS) completed an online survey measuring several mental disorder symptoms. The results revealed a prevalence of clinically elevated symptoms of 7% in posttraumatic stress disorder, 16.8% in major depressive disorder, 5.6% in anxiety, and 3.7% stress. Paramedics reported a significantly higher prevalence of clinically elevated symptoms of post-traumatic stress disorder and major depressive disorder symptoms compared to other HEMS personnel. The findings indicate a higher prevalence of some mental disorder symptoms (i.e., post-traumatic disorder, major depressive disorder) compared to existing data from a single Canadian HEMS organization. There are several psychological (e.g., coping mechanisms), organizational (e.g., time for structured debriefing), and extraneous factors (e.g., COVID-19 pandemic) that may have influenced the results. Yet, the prevalence levels remain much below those reported in on-the-ground EMS workers, which warrants further investigation.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".