A patient survey of sleep quality in the Intensive Care Unit.
Bibliographic record
Abstract
BACKGROUND: Patients in the Intensive Care Unit (ICU) experience qualitative and quantitative sleep disruption leading to sleep deprivation and adverse sequelae. Patient-related factors, environmental factors, and health-support techniques contribute to sleep disruption. This quality improvement study examines potential factors contributing to poor sleep in the ICU. METHODS: Medical and surgical patients who spent at least one night in one of two academic Canadian ICUs were asked to complete a questionnaire that explored quality and quantity of sleep, factors contributing to poor sleep, and suggested modifications to improve sleep in the ICU. Patient demographics as well as admission data were recorded. RESULTS: Study population was 116 patients (63 M:53 F). Mean age was 55.5 ± 18.1 years and APACHE II score 16.0 ± 7.9. 45.7% were mechanically ventilated, and 68.9% received intravenous sedatives and/or analgesics. Sleep quality in the ICU was rated as poor/very poor by 59% of patients compared to 24% at home; the 5 most frequently cited reasons for this were noise, pain, light, loud talking, and intravenous catheters. Patients suggested the following nocturnal modifications: closing doors/blinds, no unnecessary interruptions, sleeping pills, and dimmed lights. No significant correlations were found between perceived sleep quality and illness severity or mechanical ventilation. Patients who received intravenous sedatives reported better sleep quality (P<0.01). CONCLUSION: Patients commonly report poor sleep in the ICU related to environmental factors that are potentially modifiable.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".