Factors associated with well-being among treatment-seeking UK Veterans: A cross-sectional study
Bibliographic record
Abstract
Introduction: Comorbidity is the norm, rather than the exception, among UK treatment-seeking armed forces Veterans, who face a greater risk of experiencing mental health difficulties than the general UK population. Veterans may also experience poor overall well-being across various domains, including work-related challenges, loneliness, and instability in relationships. This study aimed to explore associations between mental ill health and mental well-being to add to the literature on the complexity of functioning and mental health presentations in clinical Veteran samples. Methods: A total of 428 UK Armed Forces Veterans (mean age 50.5 [SD 10.9] y) who received treatment for mental health difficulties at a UK Veteran mental health charity completed a survey that collected responses on the Warwick-Edinburgh Mental Well-being Scale measure of mental well-being, as well as a range of mental health and functioning outcomes. Results: Linear regression analysis revealed that well-being was significantly negatively associated with symptoms of anxiety and depression, symptoms of physical health difficulties, possible problems with anger, moral injury, and symptoms of posttraumatic stress disorder (PTSD) and complex PTSD. Functioning outcomes related to well-being were alcohol misuse, sleep problems, and loneliness. Alcohol misuse, sleep problems, and symptoms of loneliness were associated with lower well-being scores. Discussion: Implications of this pattern of relationships are discussed, including the potential benefits of a transdiagnostic and sub-clinical outcome measure for interventions targeting mental ill health, as well as positive mental health, among Veterans.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".