Clinical Epidemiology of Alcohol Use Disorders in Military Personnel versus the General Population in Canada: Épidémiologie clinique des troubles liés à la consommation d’alcool chez les militaires par opposition à la population générale du Canada
Bibliographic record
Abstract
Objectives: Research suggests a high prevalence of problematic alcohol use among military personnel relative to civilians. Our primary objectives were to compare the prevalence, correlates, help-seeking behaviors, perceived need for care, and barriers to care for alcohol use disorders (AUDs) in the Canadian Armed Forces (CAF) and the Canadian general population (CGP). Methods: Data were from 2 nationally representative surveys collected by Statistics Canada: (1) the Canadian Community Health Survey on Mental Health collected in 2012 ( N = 25,113; response rate = 68.9%) and (2) the Canadian Forces Mental Health Survey collected in 2013 ( N = 8,161; response rate = 79.8%). Descriptive statistics and logistic regression were used to examine differences in outcomes of interest associated with AUDs in the CAF and CGP. Results: The prevalence of lifetime AUDs was significantly higher in the CAF (32.0%) than the CGP (20.3%; adjusted odds ratio [AOR] = 1.14, 95% confidence interval [CI, 1.02 to 1.27]) after adjustment for sociodemographic covariates. In contrast, the past-year prevalence of AUDs was significantly lower among CAF personnel (4.5%) than civilians (3.8%; AOR = 0.78, 95% CI [0.61 to 0.99]) after adjustment for sociodemographic covariates. Child abuse history and comorbid mental disorders were strongly associated with past-year AUDs in both populations. CAF personnel compared to the CGP were more likely to perceive a need for care (AOR = 4.15, 95% CI [2.56 to 6.72]) and engage in help-seeking behaviors (significant AORs ranged from 1.85 to 5.54). CAF personnel and civilians with past-year AUDs reported different barriers to care. Conclusions: Findings argue for the value of different approaches to address unmet need for AUD care in the CAF and CGP.
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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 | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".