Patterns of alcohol use among Canadian military personnel and their associations with health and well-being.
Bibliographic record
Abstract
OBJECTIVE: Heavy drinking increases the risk of injury, adverse physical and mental health outcomes, and loss of productivity. Nonetheless, patterns of alcohol use and related symptomatology among military personnel remain poorly understood. A latent class analysis (LCA) was used to explore the presence of subgroups of alcohol users among Canadian Armed Forces (CAF) Regular Forces members. Correlates of empirically derived subgroups were further explored. METHODS: Analyses were performed on a subsample of alcohol users who participated in a 2008/09 cross-sectional survey of a stratified random sample of currently serving CAF Regular Force members (N = 1980). Multinomial logistic regression models were conducted to verify physical and mental health differences across subgroups of alcohol users. All analyses were adjusted for complex survey design. RESULTS: A 4-class solution was considered the best fit for the data. Subgroups were labeled as follows: Class 1 - Infrequent drinkers (27.2%); Class 2 - Moderate drinkers (41.5%); Class 3 - Regular binge drinkers with minimal problems (14.8%); and Class 4 - Problem drinkers (16.6%). Significant differences by age, sex, marital status, element, rank, recent serious injuries, chronic conditions, psychological distress, posttraumatic stress disorder, and depression symptoms were found across the subgroups. Problem drinkers demonstrated the most degraded physical and mental health. CONCLUSION: Findings highlight the heterogeneity of alcohol users and heavy drinkers among CAF members and the need for tailored interventions addressing high-risk alcohol use. Results have the potential to inform prevention strategies and screening efforts. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".