Experiences of trauma, depression, anxiety, and stress in western-Canadian HEMS personnel
Bibliographic record
Abstract
Mental health in first responders and other public safety personnel has received substantial research attention in the past decade. Emergency medical services (EMS) demonstrate a heightened prevalence of maladaptive mental health concerns compared to other first responders (e.g., police, fire fighters). Interestingly, there is an absence of research examining helicopter emergency medical services (HEMS) personnel, who respond to what are often life-threatening cases in challenging circumstances. Hence, the purpose of the present study was to assess the experiences of Posttraumatic Stress Disorder (PTSD) and associated mental health conditions (i.e., depression, anxiety, stress) in HEMS workers. HEMS workers from a single mid-western Canadian organization (n = 100) participated in the study. The participants completed the Posttraumatic Stress Disorder Checklist (PCL-5) and the Depression, Anxiety and Stress Scale (DASS-21) as part of an online survey. The results revealed that five per cent of HEMS personnel experienced heightened PTSD symptoms. Few participants exhibited signs of mild to severe depression, anxiety, and stress (< 17%). HEMS personnel experienced fewer mental health concerns than other first responder groups as reported in the literature; indeed, these figures are similar to levels observed within the general population. These findings may be explained by organizational or personality characteristics. Underreporting of mental health concerns may be an alternate explanation. Future qualitative and quantitative research is needed to explain and replicate the results of the present study.
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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 | 0.007 | 0.002 |
| 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".