Factors Associated with Heavy Alcohol Consumption in the U.K. Armed Forces: Data from a Health Survey of Gulf, Bosnia, and Era Veterans
Bibliographic record
Abstract
BACKGROUND: Little is known about the patterns of alcohol use in the U.K. Armed Forces or the factors associated with heavy drinking. METHODS: Analysis of existing data from the King's Military Cohort was conducted of a large, randomly selected cohort of service personnel. The original sample consisted of 8,195 service personnel who served in the U.K. Armed Forces in 1991: a third deployed to the Gulf (1990-1991), a third deployed to Bosnia (1992-1997), and the final third, an "Era" comparison group, in the Armed Forces in 1991 but not deployed. For the purposes of this study, female serving personnel were excluded. The study used a "case-control" study design nested within the above cohort; "heavy drinkers" (those who drank >30 units/week) were compared with "light drinkers" (those who drank <21 units a week). RESULTS: Heavy drinking was associated with current military service and being unmarried or separated/divorced. Heavy drinking was more common in younger personnel who had deployed to Bosnia. Those who drank heavily were also more likely to smoke; heavy drinking was associated with poorer subjective physical and mental health. CONCLUSIONS: Certain subgroups of the Armed Forces appear to be more at risk and it may be possible to target resources to such individuals to improve detection and allow prompt treatment.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".