A Sex-Specific Comparison of Major Depressive Disorder Symptomatology in the Canadian Forces and the General Population
Bibliographic record
Abstract
OBJECTIVE: To compare major depressive disorder (MDD) symptomatology within men and women in a large, representative sample of Canadian military personnel and civilians. METHOD: We used the Canadian Community Health Survey: Mental Health and Well-Being (Cycle 1.2 and Canadian Forces Supplement) (n = 36 984 and n = 8441, respectively) to compare past-year MDD symptomatology among military and civilian women, and military and civilian men. Logistic regression models were used to determine differences in the types of depressive symptoms endorsed in each group. RESULTS: Men in the military with MDD were at lower odds than men in the general population to endorse numerous symptoms of depression, such as hopelessness (adjusted odds ratio [AOR] 0.44; 99% CI 0.23 to 0.83) and inability to cope (AOR 0.53; 99% CI 0.31 to 0.92). Military women with MDD were at lower odds of thinking about their death (AOR 0.52; 99% CI 0.32 to 0.86), relative to women with MDD in the general population. CONCLUSION: Different MDD symptomatology among males and females in the military, compared with those in the general population, may reflect selection effects (for example, personality characteristics and patterns of comorbidity) or occupational experiences unique to military personnel. Future research examining the mechanisms behind MDD symptomatology in military personnel and civilians is required.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".