Conditional probability of posttraumatic stress disorder in the Canadian Armed Forces: A longitudinal study
Bibliographic record
Abstract
BACKGROUND: Posttraumatic stress disorder (PTSD) can be impacted by the type of trauma exposure. This is important to consider when evaluating the likelihood of having PTSD among Canadian veterans, which previous literature has not examined. The aim of this study was to: 1) Examine the prevalence and conditional probability of PTSD over a 16 year period by trauma type, differentiating by sex, and 2) Explore this relationship by worst trauma type. METHODS: We examined the prevalence of PTSD by sex among those with any exposure to 26 traumatic events, and used logistic regressions to examine trauma type and PTSD. The conditional probability of having PTSD over the 16-year follow-up period (2002-2018) and in the past year was calculated using population attributable fractions (PAFs). Identical analyses were used to examine the conditional probability of PTSD related to a self-reported 'worst event'. RESULTS: Over the 16-year follow-up, we found that males showed higher conditional likelihood of developing PTSD from exposure to sexual trauma while females showed a higher conditional likelihood of developing PTSD from exposure to an accidentrelated trauma. For the probability of PTSD conditional on the worst event, accidentrelated or sexual trauma in females, and sexual and deployment-related trauma in males showed the highest PAF and strongest relationship. CONCLUSION: This research supports differences in PTSD prevalence depending on the type of traumatic exposure. Importantly, different patterns of conditional probability of PTSD emerged by sex. These findings can provide important information for mental health providers within the military treating multiple traumas.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".