Veterans in crisis: Describing the complexity of presentations to a mainstream UK Veterans’ mental health service
Bibliographic record
Abstract
Introduction: Veterans may delay presentation to mental health services until they near or reach a crisis point. This study sought to understand the profiles of UK Veterans at or near crisis presenting to a Veterans' mental health service, for whom there is a paucity of data. Methods: Patient records for 177 Veterans presenting to and eligible for treatment by a specialist Veterans' mental health crisis service during the first year of its operation were accessed. Clinical presentation, factors contributing to crisis, and demographic data were extracted and analysed. Results: Veteran demographics were reflective of groups identified as being at higher risk for suicide and mental health crisis in the general population, primarily middle-aged (mean = 44.4 y) and male (93.8%). Veterans' reported mental health problems included a high prevalence of symptoms of anxiety and depression (68.4%), disordered sleep (67.2%), and posttraumatic stress disorder (PTSD; 62.1%). Psychosis was indicated to be more prevalent than among the general population. The most common reported crisis factors were suicidal ideation (85.3%), relationship strain (51.4%), and social isolation (49.7%). Reported mental health problems and crisis factors were highly comorbid. Discussion: Veterans at or near point of crisis presenting to Veteran-specific pathways in mainstream health care report a broad range of crisis factors and mental health problems. The range of presenting profiles suggest that broad-spectrum, whole-person wraparound care is suitable for addressing the needs of Veterans in crisis. Further work is required to ensure crisis services reach the whole Veteran community, including those at elevated risk.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".