Demographic characteristics, gambling engagement, mental health, and associations with harmful gambling risk among UK Armed Forces serving personnel
Bibliographic record
Abstract
INTRODUCTION: Harmful gambling negatively impacts individuals, families and communities. Growing international evidence indicates that the Armed Forces (AF) community may be at a comparatively higher risk of experiencing harm from gambling than the general population. The current study sought to identify general predictors of harmful gambling and gambling engagement among UK AF serving personnel (AFSP). METHODS: We conducted a cross-sectional, exploratory survey to identify associations between demographic factors, mental health, gambling engagement and gambling type in a sample (N=608) of AFSP. RESULTS: Most of the sample reported past-year gambling, with 23% having experienced harm. Male gender, younger age and lower educational attainment all predicted harmful gambling, as did mental health variables of prior generalised anxiety and post-traumatic stress symptomatology. Strategy-based gambling and online sports betting were also predictive of experiencing harm from gambling. CONCLUSIONS: The risk of harm from gambling is associated with demographic, mental health and gambling engagement variables among AFSP. Better understanding of these predictors is important for the development of individualised treatment approaches for harmful gambling.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".