Prevalance and determinants of antidepressant use among Canadian Forces members experiencing major depressive episodes.
Bibliographic record
Abstract
BACKGROUND: Major depression affects a significant proportion of individuals including those serving in the military; but, there is less information on the pharmacological treatment they receive. OBJECTIVES: We assessed the prevalence and determinants of past year antidepressant use among regular and reservist members of the Canadian Forces who have experienced major depressive episodes in the past 12 months. METHODS: The 2002 Canadian Community Health Survey Cycle 1.2 Canadian Forces Supplement (CCHS1.2-CFS) surveyed 8441 active members of the Canadian Forces. Individuals who reported experiencing major depressive episodes (MDE) in the past 12 months, according to the definition of the Diagnostic and Statistical Manual of Mental Disorders, 4th Edition (DSM-IV), were examined with data from the CCHS1.2-CFS. Regression models assessed sociodemographic determinants and service factors of antidepressant use employing appropriate weights and bootstrapping variance estimation methods. RESULTS: Overall, 7.4% of members of the Canadian Forces experienced MDE in the past 12 months, and of those only 32.1% reported to have taken an antidepressant. Significant predictors of antidepressant use were marital status i.e. married/common law (OR=3.6, 95%CI 2.0-6.4), widowed/separated/divorced (OR=4.0, 95%CI 2.0-8.4), and being in both combat and peacekeeping missions (OR=2.2, 95%CI 1.3-3.8). CONCLUSION: Findings highlight the characteristics that predispose individuals in the Canadian Forces with MDE to use antidepressant, and serves as a baseline to determine the effectiveness of ongoing programs for diagnosis, treatment and prevention of major depression. Continued research involving the Canadian Forces will foster better understanding of mental health outcomes and effective interventions to improve care.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".