Perception of night‐time sleep by surgical patients in an intensive care unit
Bibliographic record
Abstract
BACKGROUND: The night-time sleep of patients hospitalized in intensive care is a very important feature within the health or disease process, as it has a direct repercussion on their adequate recovery. AIMS AND OBJECTIVES: (1) To describe how surgical patients perceive their sleep in the intensive care unit; (2) to compare the subjective perception of patients with the nursing records and analyse these for the degree of agreement. DESIGN: Descriptive research. METHODS: One hundred and four surgical patients were recruited to the study. Patients completed the Richards-Campbell Sleep Questionnaire, a five-item visual analogue scale, to subjectively measure their perceived level of sleep (range 0-100 mm). The observation of patient sleep by nurses, demographic data, nursing care during the night and use of specific pharmacological treatments were also collected from the nursing records. RESULTS: The total mean score of sleep on the first post-operative night was 51.42 mm, 28% of patients had a good sleep, 46% a regular sleep and 26% a bad sleep. The sleep profile of these patients has been characterized by the patients having a light sleep, with frequent awakening and generally little difficulty to go back to sleep after the awakenings. The agreement between the nurses' perceptions of patients' sleep and the patients' perception of their sleep was tested by means of one-factor analysis of variance (p < 0.05) with a variation coefficient of 36.88%, which indicates that relative agreement was obtained. From analysing every nurse-patient perception, we obtained 44% of total agreement and 56% of disagreement. When discrepancy was found, the nurse generally overestimated the patients' perception. CONCLUSIONS: Surgical patients' perceptions of their sleep in the ICU suggest that this is inadequate. Nurses' perceptions of patients' sleep partially coincides with the latter's perception, but we have also found that the former frequently overestimate patients' sleep.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.001 | 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 teacher head, 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".