On the economics of post-traumatic stress disorder among first responders in Canada
Bibliographic record
Abstract
There is an increasing awareness of the tragic consequences of post-traumatic stress disorder (PTSD) among first responders in Canada. There is also an increasing awareness of the lack of understanding about the economic and social costs of PTSD in Canada. This article aims to briefly review the current evidence on the prevalence rates of PTSD, the economic costs associated with PTSD, and the costs and efficacy of various treatment strategies, to provide a framework for future research on the economic analysis of PTSD. Estimates suggest that as many as 2.5 million adult Canadians and 70,000 Canadian first responders have suffered from PTSD in their lifetimes. While we could not find any evidence on the economic cost of PTSD specifically, a recent estimate suggests that mental illness in the Canadian labour force results in productivity losses of $21 billion each year. Research from Australia suggests that expanded mental health care may improve the benefits of treatment over traditional care, and more cost-effectively. Given the methodological challenges in the existing studies and the paucity of evidence on Canada, more Canadian studies on prevalence, on the economic and social costs of PTSD, and on the costs and effectiveness of various treatment options are encouraged.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".