Screening does not reduce mental disorders in returning soldiers, study finds
Bibliographic record
Abstract
Screening soldiers for mental disorders after they return from battle is a waste of money, a large randomised controlled trial (RCT) has shown.1 The results of the £2m (€2.34m; $2.5m) study, funded by US taxpayers and carried out in the UK at King’s College Centre for Military Health Research, London, cast doubt on mental health screening programmes for armed service personnel introduced by several countries, including the US. The study was possible because the UK—unlike the US, Australia, Canada, and the Netherlands—has never introduced such a programme. Once screening is introduced, RCTs cannot be conducted, Simon Wessely of King’s College told a briefing at London’s Science Media Centre, because it is then impossible to create a control group by randomly allocating some participants to having no screening. “Once …
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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 | 0.012 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.035 | 0.002 |
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".