Sleep Promotion among Critically Ill Patients: Earplugs/Eye Mask versus Ocean Sound—A Randomized Controlled Trial Study
Bibliographic record
Abstract
Background. Poor sleep quality is common in the intensive care unit (ICU), where several factors including environmental factors contribute to sleep deprivation. Objective. This study aims to assess and compare the effectiveness of earplugs and eye mask versus ocean sound on sleep quality among ICU patients. Design. A true experimental crossover design was used. Setting. Medical ICU of the Maharishi Markandeshwar Institute of Medical Sciences and Research Hospital, Mullana, India. Participants. Sixty-eight patients admitted in the medical ICU were randomly allocated by lottery methods into group 1 and group 2. Methods. Nocturnal nine-hour (10 : 00 pm to 7 : 00 am) for a four-night period were measured. Earplugs and eye mask versus ocean sound were crossed over between two groups. Subjective sleep quality of four nights was assessed using a structured sleep quality scale. Scores for each question range from 0 to 3, with a higher score indicating poor sleep quality. Results. Repeated measures ANOVA showed that there was a significant change in the sleep quality score ( p = 0.001 ), which showed that sleep quality score was improved after the administration of earplugs and eye mask and ocean sound. Fisher’s LSD post hoc comparison showed a significant difference ( p = 0.001 ). Conclusion. Earplugs and eye mask were better than ocean sound in improving sleep quality. Earplugs, eye mask, and ocean sound are safe and cost effective, which could be used as an adjuvant to pharmacological interventions to improve sleep quality among ICU patients. However, further research in this area needs to be conducted. This trial is registered with NCT03215212.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".