Risk factors for heat illness among British soldiers in the hot Collective Training Environment
Bibliographic record
Abstract
BACKGROUND: Heat illness is a preventable disorder in military populations. Measures that protect vulnerable individuals and contribute to effective Immediate Treatment may reduce the impact of heat illness, but depend upon adequate understanding and awareness among Commanders and their troops. OBJECTIVE: To assess risk factors for heat illness in British soldiers deployed to the hot Collective Training Environment (CTE) and to explore awareness of Immediate Treatment responses. METHODS: An anonymous questionnaire was distributed to British soldiers deployed in the hot CTEs of Kenya and Canada. Responses were analysed to determine the prevalence of individual (Intrinsic) and Command-practice (Extrinsic) risk factors for heat illness and the self-reported awareness of key Immediate Treatment priorities (recognition, first aid and casualty evacuation). RESULTS: The prevalence of Intrinsic risk factors was relatively low in comparison with Extrinsic risk factors. The majority of respondents were aware of key Immediate Treatment responses. The most frequently reported factors in each domain were increased risk by body composition scoring, inadequate time for heat acclimatisation and insufficient briefing about casualty evacuation. CONCLUSIONS: Novel data on the distribution and scale of risk factors for heat illness are presented. A collective approach to risk reduction by the accumulation of 'marginal gains' is proposed for the UK military. This should focus on limiting Intrinsic risk factors before deployment, reducing Extrinsic factors during training and promoting timely Immediate Treatment responses within the hot CTE.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".