Associations among child abuse history, deployment‐related traumatic events, mental disorders, and suicidal behaviors in Canadian Regular Force personnel
Bibliographic record
Abstract
Increasing attention has been focused on suicidal behavior among military personnel. Exposure to deployment-related traumatic events (DRTEs) and child abuse (CA) both have been associated with mental disorders and suicidal behaviors among military personnel. Thus, the primary objectives of this study were to examine (a) sex differences in CA history and DRTEs, past-year mental disorders, and past-year suicide-related outcomes and (b) independent, cumulative, and interactive effects of CA history and DRTEs on past-year mental disorders and suicide outcomes among Canadian military personnel. Data were from the representative Canadian Forces Mental Health Survey collected in 2013 (N = 8,161; response rate = 79.8%). The results indicated a high prevalence of trauma exposure, with sex differences noted for specific trauma types. Both CA history and DRTEs were strongly associated with mental disorders, CA history: aORs = 1.60-2.44; DRTEs; aORs = 1.67-3.88. Cumulative, but not interactive, effects were noted for the effects of CA history and DRTEs on most mental disorders. Associations between CA history and DRTEs on suicide outcomes were largely indirect via their impact on mental disorders. Information regarding the role of specific types of predeployment trauma on mental disorders and suicidal behavior can be used to develop more targeted prevention and intervention strategies aimed at improving the mental health of military personnel.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".