Loneliness among UK Veterans: Associations with quality of life, alcohol misuse, and perceptions of partner drinking
Bibliographic record
Abstract
Introduction: Loneliness occurs when there is a disparity between the quantity and quality of social relationships people have and the ones they want. Research shows loneliness is negatively associated with quality of life and alcohol misuse, two common issues for military Veterans. Loneliness can also be affected by partner drinking, particularly if it does not match Veterans' drinking behaviour. This study aimed to explore 1) the association between loneliness, quality of life, and alcohol misuse, and 2) the association between perceived partner drinking and loneliness in a sample of treatment-seeking UK Veterans. Methods: A total of 163 treatment-seeking UK Veterans completed a self-report questionnaire via the DrinksRation smartphone application. Loneliness was measured using the 3-item UCLA Loneliness Scale. Linear regressions explored associations between loneliness, quality of life, and alcohol misuse. Logistic regressions explored associations between perceived partner drinking and loneliness. Results: Almost two-thirds of participants reported feeling lonely (65.6%). Unadjusted linear regressions showed lonely Veterans had lower quality-of-life scores across all domains and higher alcohol misuse scores than non-lonely Veterans. After full adjustment, loneliness was significantly associated only with the physical health, social relationships, and quality-of-life domains. Logistic regressions revealed no significant associations between perceptions of partner drinking and loneliness. Discussion: This study found lonely treatment-seeking Veterans had poorer quality of life and higher alcohol misuse than non-lonely counterparts. Innovative ways to reduce loneliness and improve social connectedness for Veterans are required, particularly for those with mental health needs and who drink heavily.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.000 | 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".