Potentially modifiable risk factors for mental health problems in deployed UK maritime forces
Bibliographic record
Abstract
Military deployments and mental health problems (MHPs) have often been studied, but only infrequently in the deployed environment, where MHP-related impairments would be particularly consequential. Fewer still have looked at deployed sailors, so Whybrow et al 's1 cross-sectional survey of MHPs among Royal Navy personnel deployed at sea is a welcome addition to the literature. They found that 41.2% had common mental disorder symptoms (ie, mood, anxiety or neurotic spectrum disorders), 7.8% probable post-traumatic stress disorder (PTSD), and 17.4% potentially harmful alcohol use. These prevalence rates are strikingly higher than those in deployed UK land-based forces using comparable methods; common mental disorder rates varied from 16.0% to 20.8% and probable PTSD from 1.9% to 3.4%.2 3 An earlier study of deployed Royal Navy personnel documented similarly elevated rates, suggesting that the present findings are not a chance occurrence.4 Past research has focused on operational trauma as the primary driver of poor mental health in military personnel, both on deployment and afterwards. The low trauma exposure in the present study suggests other attributions for the high levels of MHPs, such as premilitary characteristics and experiences, …
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".