Physical sleeping environment is related to insomnia risk and measures of readiness in US army special operations soldiers
Bibliographic record
Abstract
BACKGROUND: ' or at one's home base. The physical sleeping environment, which is often poor in military-provided housing or barracks, may contribute to poor sleep quality in soldiers. The current study aimed to assess whether the sleeping environment in garrison is related to sleep quality, insomnia risk and military readiness. METHODS: Seventy-four US army special operations soldiers participated in a cross-sectional study. Soldiers were queried on their sleeping surface comfort and the frequency of being awakened at night by excess light, abnormal temperatures and noise. Subjective sleep quality and insomnia symptoms were also queried, via the Pittsburgh Sleep Quality Index and Insomnia Severity Index, respectively. Lastly, measures of soldier readiness, including morale, motivation, fatigue, mood and bodily pain, were assessed. RESULTS: Soldiers reporting temperature-related and light-related awakenings had poorer sleep quality higher fatigue and higher bodily pain than soldiers without those disturbances. Lower ratings of sleeping surface comfort were associated with poorer sleep quality and lower motivation, lower morale, higher fatigue and higher bodily pain. Each 1-point increase in sleeping surface comfort decreased the risk for a positive insomnia screen by 38.3%, and the presence of temperature-related awakenings increased risk for a positive insomnia screen by 78.4%. Those living on base had a poorer sleeping environment than those living off base. CONCLUSION: Optimising the sleep environment-particularly in on-base, military-provided housing-may improve soldier sleep quality, and readiness metrics. Providers treating insomnia in soldiers should rule out environment-related sleep disturbances prior to beginning more resource-intensive treatment.
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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 | 0.000 | 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".