Comorbidity Patterns of Psychiatric Conditions in Canadian Armed Forces Personnel
Bibliographic record
Abstract
Objective: Posttraumatic stress disorder (PTSD) is often accompanied by other mental health conditions, including major depressive disorder (MDD), substance misuse disorders, and anxiety disorders. The objective of the current study is to delineate classes of comorbidity and investigate predictors of comorbidity classes amongst a sample of Canadian Armed Forces (CAF) Regular Force personnel. Methods: Latent class analyses (LCAs) were applied to cross-sectional data obtained between April and August 2013 from a nationally representative random sample of 6700 CAF Regular Force personnel who deployed to the mission in Afghanistan. Results: MDD was the most common diagnosis (8.0%), followed by PTSD (5.3%) and generalized anxiety disorder (4.7%). Of those with a mental health condition, LCA revealed 3 classes of comorbidity: a highly comorbid class (8.3%), a depressed-only class (4.6%), and an alcohol use–only class (3.1%). Multinomial logit regression showed that women (adjusted relative risk ratio [ARRR] = 2.77; 95% CI, 2.13 to 3.60; P < 0.01) and personnel reporting higher trauma exposure (ARRR = 4.18; 95% CI, 3.13 to 5.57; P < 0.01) were at increased risk of membership in the comorbid class compared to those without a mental health condition. When compared to those with no mental health condition, experiencing childhood abuse increased the risk of being in any comorbidity class. Conclusions: Results provide further evidence to support screening for and treatment of comorbid mental health conditions. The role of sex, childhood abuse, and combat deployment in determining class membership may also prove valuable for clinicians treating military-related mental health conditions.
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 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".