PREVALENCE OF POSTTRAUMATIC STRESS DISORDER AMONG CURRENT AND FORMER MEMBERS OF THE CANADIAN ARMED FORCES
Bibliographic record
Abstract
Objective: This study aims to synthesize the literature regarding the prevalence of posttraumatic stress disorder (PTSD) among current and former members of the Canadian Armed Forces (CAF). Methods: We searched EMBASE, MEDLINE, PsycINFO, and CINAHL from inception until June 2024 for studies reporting on the prevalence of PTSD among Canadian military personnel and veterans. We performed a Freeman-Tukey double arcsine transformation to stabilize the variance and then pooled the prevalence of PTSD across studies using a random-effects model. We assessed the credibility of subgroup effects using the Instrument to Assess the Credibility of Effect Modification Analyses (ICEMAN) tool. We used the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) approach to assess the certainty of evidence. Results: We included 34 observational studies. The pooled prevalence of PTSD was 35.5%, however we found evidence of credible subgroup effects based on representativeness of the study population, and proportion of veterans included. High certainty evidence from 10 representative studies (n=58,060) suggests the prevalence of PTSD among active CAF members is 5.8% (95% CI: 4.1 to 7.8%). Moderate certainty evidence from 7 representative studies (n=13,194) suggests the prevalence of PTSD is probably 15.4% (95% CI: 7.5–25.4%) among CAF veterans. Conclusions: We found high certainty evidence that 6 in every 100 CAF members live with PTSD, and moderate certainty evidence that 15 in every 100 Canadian veterans live with PTSD. These findings highlight the need for PTSD screening and access to care for current and former CAF members, and further research to identify evidence-based care.
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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 | 0.042 | 0.188 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.025 | 0.020 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".