Prevalence Comparison of Past-year Mental Disorders and Suicidal Behaviours in the Canadian Armed Forces and the Canadian General Population
Bibliographic record
Abstract
OBJECTIVE: Military personnel in Canada and elsewhere have been found to have higher rates of certain mental disorders relative to their corresponding general populations. However, published Canadian data have only adjusted for age and sex differences between the populations. Additional differences in the sociodemographic composition, labour force characteristics, and childhood trauma exposure in the populations could be driving these prevalence differences. Our objective is to compare the prevalence of past-year mental disorders and suicidal behaviours in the Canadian Armed Forces Regular Force with the rates in a representative, matched sample of Canadians in the general population (CGP). METHODS: Data sources were the 2013 Canadian Forces Mental Health Survey and the 2012 Canadian Community Health Survey-Mental Health. CGP sample was restricted to match the age range, employment status, and history of chronic conditions of Regular Force personnel. An iterative proportional fitting method was used to approximate the marginal distribution of sociodemographic and childhood trauma variables in both samples. RESULTS: Relative to the matched CGP, Regular Force personnel had significantly higher rates of past-year major depressive episode, generalized anxiety disorder, and suicide ideation. However, lower rates of alcohol use disorder were seen in Regular Force personnel relative to the matched CGP sample. CONCLUSIONS: Factors other than differences in sociodemographic composition and history of childhood trauma account for the excess burden of mental disorders and suicidal behaviours in the Canadian Armed Forces. Explanations to explore in future research include occupational trauma, selection effects, and differences in the context of administration of the 2 surveys.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".