Predictors of Posttraumatic Stress Disorder, Depression, and Suicidal Ideation among Canadian Forces Personnel in a National Canadian Military Health Survey
Bibliographic record
Abstract
Despite efforts to elucidate the relationship between traumatic event exposure and adverse mental health outcomes, our ability to understand why only some trauma-exposed individuals become emotionally affected remains challenged. The aim of the current study is to determine the relations between social support, religiosity, and number of lifetime traumatic events experienced on past-12 month posttraumatic stress disorder (PTSD), depression, and suicidal ideation (SI) in a nationally representative sample of Canadian Forces personnel. The current study used data from the Canadian Community Health Survey Cycle 1.2 – Canadian Forces Supplement. The impact of a number of predictive and mediating factors was assessed using structural equation modeling. Social support and number of lifetime traumatic events experienced were significant predictors of past-year PTSD, depression, and SI; however PTSD did not mediate the relationship between number of traumatic events and SI nor between social support and SI. Conversely, depression mediated the relationship between number of traumatic events and SI. Possible mechanisms for these findings and their implications are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".